Matlab for Machine Learning Essentials: Unsupervised Learning Models

This blog teaches you how to use Matlab for unsupervised learning, a branch of machine learning that deals with finding patterns in unlabeled data.

1. Introduction

Machine learning is a branch of artificial intelligence that deals with creating systems that can learn from data and make predictions or decisions. Machine learning can be divided into two main categories: supervised learning and unsupervised learning.

In supervised learning, you have a set of labeled data, where each input has a corresponding output or target. The goal is to train a model that can map the inputs to the outputs and generalize to new data. Examples of supervised learning tasks are classification and regression.

In unsupervised learning, you have a set of unlabeled data, where you do not know the outputs or targets. The goal is to find patterns or structure in the data without any guidance. Examples of unsupervised learning tasks are clustering, dimensionality reduction, and anomaly detection.

In this blog, you will learn how to perform unsupervised learning in Matlab, a popular programming language and platform for numerical computing and data analysis. Matlab has many built-in functions and toolboxes that can help you implement and evaluate unsupervised learning models.

You will learn how to:

Prepare and visualize your data for unsupervised learning
Train and compare different clustering algorithms, such as k-means, hierarchical clustering, and Gaussian mixture models
Train and compare different dimensionality reduction algorithms, such as principal component analysis, linear discriminant analysis, and t-distributed stochastic neighbor embedding
Train and compare different anomaly detection algorithms, such as one-class support vector machines, local outlier factor, and isolation forest
Evaluate your unsupervised learning models using validation metrics and visualization techniques

By the end of this blog, you will have a solid understanding of the basics of unsupervised learning and how to apply it to your own data using Matlab.

Are you ready to dive into the world of unsupervised learning? Let’s get started!

2. What is Unsupervised Learning?

Unsupervised learning is a type of machine learning that deals with finding patterns or structure in unlabeled data. Unlike supervised learning, where you have a predefined output or target for each input, unsupervised learning does not require any guidance or supervision. The goal is to discover the hidden features or characteristics of the data that can help you understand it better or perform some tasks.

Unsupervised learning can be useful for many purposes, such as:

Exploring and analyzing the data to gain insights and identify potential problems or opportunities
Reducing the dimensionality or complexity of the data to make it easier to process or visualize
Segmenting or grouping the data into meaningful categories or clusters based on their similarities or differences
Detecting outliers or anomalies in the data that deviate from the normal or expected behavior
Generating new data or features that can enhance the existing data or create novel applications

Unsupervised learning can be applied to various types of data, such as numerical, categorical, textual, image, audio, video, or mixed data. However, different types of data may require different types of unsupervised learning algorithms or techniques.

What are the main types of unsupervised learning algorithms or techniques? How do they work and what are their advantages and disadvantages? In the next section, you will learn about the most common types of unsupervised learning: clustering, dimensionality reduction, and anomaly detection.

2.1. Types of Unsupervised Learning

There are many types of unsupervised learning algorithms or techniques, but the most common ones are clustering, dimensionality reduction, and anomaly detection. In this section, you will learn what each of these types does, how they work, and what are their advantages and disadvantages.

Clustering

Clustering is a type of unsupervised learning that aims to group the data into meaningful categories or clusters based on their similarities or differences. Clustering can help you discover the underlying structure or patterns of the data, as well as identify outliers or anomalies that do not belong to any cluster.

There are many clustering algorithms, but they can be broadly classified into two categories: hierarchical clustering and partitioning clustering. Hierarchical clustering builds a tree-like structure of clusters, where each cluster can be further divided into subclusters. Partitioning clustering divides the data into a fixed number of clusters, where each data point belongs to one and only one cluster.

Some of the most popular clustering algorithms are:

K-means: A partitioning algorithm that assigns each data point to the nearest cluster center, and iteratively updates the cluster centers until convergence.
Hierarchical clustering: A hierarchical algorithm that either merges the closest clusters (agglomerative) or splits the largest clusters (divisive) until a desired number of clusters is reached.
Gaussian mixture models: A probabilistic algorithm that assumes each cluster follows a Gaussian distribution, and estimates the parameters of each cluster using the expectation-maximization algorithm.

Clustering has many advantages, such as:

It is easy to implement and interpret
It can handle large and high-dimensional data
It can reveal hidden patterns or structure of the data
It can identify outliers or anomalies in the data

Clustering also has some disadvantages, such as:

It may require prior knowledge of the number of clusters or the cluster structure
It may be sensitive to noise, outliers, or initialization
It may not find the optimal or global solution
It may not handle complex or overlapping clusters well

Dimensionality Reduction

Dimensionality reduction is a type of unsupervised learning that aims to reduce the number of features or dimensions of the data, while preserving as much information as possible. Dimensionality reduction can help you simplify the data, improve the computational efficiency, and visualize the data in lower dimensions.

There are many dimensionality reduction algorithms, but they can be broadly classified into two categories: linear dimensionality reduction and nonlinear dimensionality reduction. Linear dimensionality reduction assumes that the data lies on or near a linear subspace, and projects the data onto a lower-dimensional linear subspace. Nonlinear dimensionality reduction assumes that the data lies on or near a nonlinear manifold, and maps the data onto a lower-dimensional nonlinear manifold.

Some of the most popular dimensionality reduction algorithms are:

Principal component analysis (PCA): A linear algorithm that finds the orthogonal directions of maximum variance in the data, and projects the data onto a lower-dimensional subspace spanned by these directions.
Linear discriminant analysis (LDA): A linear algorithm that finds the directions that maximize the separation between different classes in the data, and projects the data onto a lower-dimensional subspace spanned by these directions.
T-distributed stochastic neighbor embedding (t-SNE): A nonlinear algorithm that preserves the local distances or similarities between the data points, and maps the data onto a lower-dimensional manifold using a probabilistic model.

Dimensionality reduction has many advantages, such as:

It can reduce the noise, redundancy, or complexity of the data
It can improve the computational efficiency and performance of other algorithms
It can visualize the data in lower dimensions
It can reveal the intrinsic or latent features of the data

Dimensionality reduction also has some disadvantages, such as:

It may lose some information or interpretability of the data
It may require prior knowledge of the target dimension or the manifold structure
It may be sensitive to parameters, scaling, or outliers
It may not preserve the global or nonlinear structure of the data well

Anomaly Detection

Anomaly detection is a type of unsupervised learning that aims to identify the data points that deviate from the normal or expected behavior. Anomaly detection can help you detect errors, frauds, faults, or intrusions in the data, as well as understand the causes or consequences of these anomalies.

There are many anomaly detection algorithms, but they can be broadly classified into two categories: statistical anomaly detection and distance-based anomaly detection. Statistical anomaly detection assumes that the data follows a certain distribution, and identifies the data points that have a low probability of occurring under this distribution. Distance-based anomaly detection assumes that the data points are clustered in a certain way, and identifies the data points that are far away from their nearest neighbors or clusters.

Some of the most popular anomaly detection algorithms are:

One-class support vector machines (OCSVM): A statistical algorithm that finds a boundary that separates the normal data from the outliers, and assigns a score to each data point based on its distance from the boundary.
Local outlier factor (LOF): A distance-based algorithm that measures the local density of each data point, and assigns a score to each data point based on its deviation from the local density of its neighbors.
Isolation forest: A distance-based algorithm that randomly splits the data along different features, and assigns a score to each data point based on the number of splits required to isolate it.

Anomaly detection has many advantages, such as:

It can identify the abnormal or suspicious data points
It can help prevent or mitigate the negative impacts of anomalies
It can provide insights or explanations for the anomalies
It can handle high-dimensional or complex data

Anomaly detection also has some disadvantages, such as:

It may require prior knowledge of the distribution or the cluster structure of the data
It may be sensitive to parameters, noise, or outliers
It may have a high false positive or false negative rate
It may not handle dynamic or evolving data well

Now that you have learned about the main types of unsupervised learning, you may wonder how to apply them to your own data using Matlab. In the next section, you will learn how to perform unsupervised learning in Matlab using some of the built-in functions and toolboxes.

2.2. Applications of Unsupervised Learning

Unsupervised learning has many applications in various domains and industries, such as data mining, bioinformatics, computer vision, natural language processing, cybersecurity, and more. In this section, you will learn about some of the common and interesting applications of unsupervised learning, and how they can benefit from the techniques you learned in the previous section.

Data Mining

Data mining is the process of extracting useful information or knowledge from large and complex data sets. Unsupervised learning can help data mining in many ways, such as:

Exploring and analyzing the data to find patterns, trends, correlations, or outliers
Reducing the dimensionality or complexity of the data to make it easier to process or visualize
Segmenting or grouping the data into meaningful categories or clusters based on their similarities or differences
Detecting outliers or anomalies in the data that deviate from the normal or expected behavior
Generating new data or features that can enhance the existing data or create novel applications

Some examples of data mining applications that use unsupervised learning are:

Customer segmentation: Clustering customers based on their demographics, preferences, behaviors, or transactions to understand their needs, preferences, or motivations, and provide personalized services or recommendations
Market basket analysis: Finding associations or rules between items that are frequently purchased together by customers, and using them to cross-sell or upsell products or services
Topic modeling: Finding the main topics or themes that are discussed in a collection of documents, such as news articles, blogs, or social media posts, and using them to summarize, categorize, or analyze the content
Image compression: Reducing the size or quality of an image by removing redundant or irrelevant pixels or colors, and using them to store, transmit, or display the image more efficiently
Face recognition: Identifying or verifying the identity of a person based on their facial features, and using them to grant access, track attendance, or find matches

Bioinformatics

Bioinformatics is the application of computational methods to analyze biological data, such as DNA, RNA, proteins, or cells. Unsupervised learning can help bioinformatics in many ways, such as:

Exploring and analyzing the data to find patterns, similarities, differences, or functions
Reducing the dimensionality or complexity of the data to make it easier to process or visualize
Segmenting or grouping the data into meaningful categories or clusters based on their similarities or differences
Detecting outliers or anomalies in the data that deviate from the normal or expected behavior
Generating new data or features that can enhance the existing data or create novel applications

Some examples of bioinformatics applications that use unsupervised learning are:

Gene expression analysis: Measuring the activity or expression of genes in different conditions, such as tissues, diseases, or treatments, and using them to understand the function, regulation, or interaction of genes
Protein structure prediction: Predicting the three-dimensional structure or shape of a protein based on its amino acid sequence, and using it to understand the function, interaction, or evolution of proteins
Metagenomics: Studying the genetic material of microorganisms in a given environment, such as soil, water, or human body, and using it to understand the diversity, function, or interaction of microorganisms
Cancer detection: Identifying or classifying cancer cells or tumors based on their molecular or genetic features, and using them to diagnose, treat, or prevent cancer
Drug discovery: Finding or designing new drugs or compounds that can interact with a target protein or molecule, and using them to treat or cure diseases

Computer Vision

Computer vision is the field of computer science that deals with enabling machines to see, understand, and interact with images or videos. Unsupervised learning can help computer vision in many ways, such as:

Exploring and analyzing the data to find patterns, features, or objects
Reducing the dimensionality or complexity of the data to make it easier to process or visualize
Segmenting or grouping the data into meaningful categories or clusters based on their similarities or differences
Detecting outliers or anomalies in the data that deviate from the normal or expected behavior
Generating new data or features that can enhance the existing data or create novel applications

Some examples of computer vision applications that use unsupervised learning are:

Object detection: Locating or identifying objects of interest in an image or video, such as faces, cars, or animals, and using them to perform tasks such as face recognition, vehicle tracking, or animal counting
Image segmentation: Dividing an image into regions or pixels that belong to the same object or category, such as foreground, background, or edges, and using them to perform tasks such as image editing, enhancement, or compression
Image synthesis: Creating new images or modifying existing images based on some input or condition, such as text, sketch, or style, and using them to perform tasks such as image captioning, inpainting, or style transfer
Image retrieval: Finding or retrieving images that are similar or relevant to a given query or example, such as a keyword, a color, or an image, and using them to perform tasks such as image search, recommendation, or classification
Video analysis: Analyzing the content or context of a video, such as actions, events, or scenes, and using them to perform tasks such as video summarization, captioning, or understanding

Natural Language Processing

Natural language processing is the field of computer science that deals with enabling machines to understand, generate, and interact with natural language, such as text or speech. Unsupervised learning can help natural language processing in many ways, such as:

Exploring and analyzing the data to find patterns, features, or meanings
Reducing the dimensionality or complexity of the data to make it easier to process or visualize
Segmenting or grouping the data into meaningful categories or clusters based on their similarities or differences
Detecting outliers or anomalies in the data that deviate from the normal or expected behavior
Generating new data or features that can enhance the existing data or create novel applications

Some examples of natural language processing applications that use unsupervised learning are:

Word embedding: Representing words or phrases as vectors or numbers that capture their semantic or syntactic properties, and using them to perform tasks such as word similarity, analogy, or sentiment analysis
Text summarization: Generating a short and concise summary of a long and complex text, such as a document, an article, or a review, and using it to perform tasks such as information extraction, retrieval, or comprehension
Text generation: Creating new text or modifying existing text based on some input or condition, such as a topic, a keyword, or a style, and using it to perform tasks such as text completion, paraphrasing, or translation
Text clustering: Grouping texts or documents into categories or clusters based on their content or topic, and using them to perform tasks such as text classification, recommendation, or analysis
Speech recognition: Converting speech or audio signals into text or commands, and using them to perform tasks such as speech transcription, translation, or understanding

Cybersecurity

Cybersecurity is the field of computer science that deals with protecting information and systems from cyberattacks or threats. Unsupervised learning can help cybersecurity in many ways, such as:

Exploring and analyzing the data to find patterns, features, or anomalies
Reducing the dimensionality or complexity of the data to make it easier to process or visualize
Segmenting or grouping the data into meaningful categories or clusters based on their similarities or differences
Detecting outliers or anomalies in the data that deviate from the normal or expected behavior
Generating new data or features that can enhance the existing data or create novel applications

Some examples of cybersecurity applications that use unsupervised learning are:

Network intrusion detection: Monitoring and analyzing network traffic or activity to detect or prevent unauthorized or malicious access, and using them to perform tasks such as network security, forensics, or defense
Malware analysis: Analyzing and identifying malicious software or code that can harm or compromise a system, and using them to perform tasks such as malware detection, classification, or removal
Spam filtering: Filtering or blocking unwanted or unsolicited messages or emails, and using them to perform tasks such as spam detection, prevention, or analysis
Phishing detection: Detecting or preventing fraudulent or deceptive attempts to obtain sensitive or personal information, such as passwords, credit card numbers, or bank accounts, and using them to perform tasks such as phishing detection, prevention, or analysis
Password cracking: Breaking or guessing passwords or encryption keys that protect or secure a system, and using them to

3. How to Perform Unsupervised Learning in Matlab

In this section, you will learn how to perform unsupervised learning in Matlab using the built-in functions and toolboxes that are available for different types of unsupervised learning tasks. You will also learn how to use some of the most popular and widely used unsupervised learning algorithms, such as k-means, principal component analysis, and one-class support vector machines.

Before you can apply any unsupervised learning algorithm to your data, you need to prepare and visualize your data properly. This will help you understand the characteristics and distribution of your data, as well as identify any potential issues or challenges that may affect your unsupervised learning results.

Some of the common steps that you need to perform for data preparation and visualization are:

Loading and importing your data into Matlab from various sources, such as files, databases, or web services
Exploring and summarizing your data using descriptive statistics, such as mean, median, standard deviation, and correlation
Cleaning and preprocessing your data to handle missing values, outliers, noise, or errors
Transforming and scaling your data to make it suitable for unsupervised learning algorithms, such as normalizing, standardizing, or encoding
Visualizing your data using various plots and graphs, such as histograms, scatter plots, box plots, or heat maps

Matlab provides many functions and tools that can help you perform these steps easily and efficiently. For example, you can use the readtable function to import data from a spreadsheet file, the summary function to get a quick overview of your data, the fillmissing function to fill in missing values, the rescale function to normalize your data, and the plot function to create various types of plots.

Here is an example of how you can load, explore, and visualize a sample dataset in Matlab using some of these functions:

% Load the sample dataset 'fisheriris' that contains measurements of iris flowers
load fisheriris

% Convert the data to a table and add variable names
data = array2table(meas,'VariableNames',{'SepalLength','SepalWidth','PetalLength','PetalWidth'});

% Add the species name as a categorical variable
data.Species = categorical(species);

% Display the first five rows of the data
head(data)

% Get the summary statistics of the data
summary(data)

% Plot the pairwise scatter plots of the variables, colored by species
gplotmatrix(data(:,1:4),[],data.Species,[],[],[],[],[],data.Properties.VariableNames)

% Plot the box plots of the variables, grouped by species
figure
boxplot(data.SepalLength,data.Species)
xlabel('Species')
ylabel('Sepal Length')
title('Box Plot of Sepal Length by Species')

As you will see, the data contains 150 observations of four numerical variables (sepal length, sepal width, petal length, and petal width) and one categorical variable (species name). The summary statistics show the mean, median, standard deviation, and range of each variable, as well as the number and percentage of unique values for the categorical variable. The scatter plots show the relationship between each pair of variables, as well as the distribution of each variable by species. The box plots show the variation of each variable by species, as well as the outliers and extreme values.

By visualizing your data, you can get a better understanding of its characteristics and structure, as well as identify any patterns or clusters that may exist in the data. This can help you choose the appropriate unsupervised learning algorithm for your data and set the parameters accordingly.

In the next subsection, you will learn how to perform clustering on your data using Matlab.

3.1. Data Preparation and Visualization

Data preparation and visualization are essential steps for any machine learning task, especially for unsupervised learning. By preparing and visualizing your data, you can ensure that your data is clean, consistent, and suitable for the unsupervised learning algorithms that you want to apply. You can also gain insights into the characteristics and distribution of your data, as well as identify any patterns or clusters that may exist in the data.

In this subsection, you will learn how to perform some common data preparation and visualization tasks in Matlab using the built-in functions and tools that are available. You will learn how to:

Load and import your data into Matlab from various sources, such as files, databases, or web services
Explore and summarize your data using descriptive statistics, such as mean, median, standard deviation, and correlation
Clean and preprocess your data to handle missing values, outliers, noise, or errors
Transform and scale your data to make it suitable for unsupervised learning algorithms, such as normalizing, standardizing, or encoding
Visualize your data using various plots and graphs, such as histograms, scatter plots, box plots, or heat maps

By the end of this subsection, you will be able to prepare and visualize your data for unsupervised learning in Matlab using some simple and effective commands.

Let’s start with loading and importing your data into Matlab.

3.2. Clustering Algorithms

Clustering is one of the most common and widely used types of unsupervised learning. Clustering is the process of grouping data points into meaningful categories or clusters based on their similarities or differences. The goal is to find the optimal number and structure of clusters that best represent the data and reveal its hidden patterns.

Clustering can be useful for many purposes, such as:

Discovering the natural or latent groups or segments in the data, such as customer segments, market segments, or product categories
Reducing the complexity or diversity of the data by creating homogeneous or similar groups of data points
Enhancing the performance or accuracy of other machine learning tasks, such as classification, regression, or recommendation systems, by using the cluster labels or features as inputs or outputs
Explaining or interpreting the data by assigning meaningful labels or names to the clusters based on their characteristics or properties

Clustering can be applied to various types of data, such as numerical, categorical, textual, image, audio, video, or mixed data. However, different types of data may require different types of clustering algorithms or techniques.

There are many clustering algorithms or techniques that have been developed and used for different purposes and scenarios. Some of the most popular and widely used clustering algorithms are:

K-means: A simple and fast algorithm that partitions the data into k clusters based on the distance or similarity between the data points and the cluster centers or centroids
Hierarchical clustering: A flexible and intuitive algorithm that creates a hierarchical tree or dendrogram of clusters based on the distance or similarity between the data points or the clusters
Gaussian mixture models: A probabilistic and generative algorithm that models the data as a mixture of k Gaussian distributions and assigns each data point a probability of belonging to each cluster

Matlab provides many functions and tools that can help you perform clustering on your data using these algorithms. For example, you can use the kmeans function to perform k-means clustering, the linkage and cluster functions to perform hierarchical clustering, and the fitgmdist and posteriors functions to perform Gaussian mixture models clustering.

Here is an example of how you can perform k-means clustering on the sample dataset ‘fisheriris’ that you loaded and visualized in the previous subsection:

% Load the sample dataset 'fisheriris' that contains measurements of iris flowers
load fisheriris

% Convert the data to a table and add variable names
data = array2table(meas,'VariableNames',{'SepalLength','SepalWidth','PetalLength','PetalWidth'});

% Add the species name as a categorical variable
data.Species = categorical(species);

% Perform k-means clustering on the numerical variables with k = 3
[idx,centroids] = kmeans(data(:,1:4),3);

% Add the cluster labels as a categorical variable
data.Cluster = categorical(idx);

% Plot the pairwise scatter plots of the variables, colored by cluster
gplotmatrix(data(:,1:4),[],data.Cluster,[],[],[],[],[],data.Properties.VariableNames)

% Plot the cluster centroids on the scatter plot of the first two variables
figure
scatter(data.SepalLength,data.SepalWidth,[],data.Cluster)
hold on
plot(centroids(:,1),centroids(:,2),'kx','MarkerSize',15,'LineWidth',3)
hold off
xlabel('Sepal Length')
ylabel('Sepal Width')
title('K-means Clustering of Iris Data')
legend('Cluster 1','Cluster 2','Cluster 3','Centroids')

As you will see, the k-means algorithm has partitioned the data into three clusters based on the distance between the data points and the cluster centroids. The scatter plots show the relationship between each pair of variables, as well as the distribution of each variable by cluster. The cluster centroids are marked by black crosses on the scatter plot of the first two variables. You can also compare the cluster labels with the species labels to see how well the k-means algorithm has captured the natural groups in the data.

By performing clustering on your data, you can discover the hidden structure and patterns in your data, as well as create meaningful categories or segments that can help you understand or analyze your data better.

In the next subsection, you will learn how to perform dimensionality reduction on your data using Matlab.

3.3. Dimensionality Reduction Algorithms

Dimensionality reduction is another important and useful type of unsupervised learning. Dimensionality reduction is the process of reducing the number of features or variables in the data while preserving as much information as possible. The goal is to find a lower-dimensional representation of the data that can capture its essential characteristics or structure.

Dimensionality reduction can be beneficial for many reasons, such as:

Improving the computational efficiency and performance of other machine learning tasks, such as clustering, classification, or regression, by reducing the complexity and size of the data
Enhancing the visualization and interpretation of the data by projecting it into a lower-dimensional space, such as two or three dimensions, where it can be easily plotted and analyzed
Removing the noise or redundancy in the data by eliminating the irrelevant or correlated features that may affect the quality or accuracy of the results
Extracting the latent or hidden features or factors that can explain the variation or behavior of the data, such as principal components, latent variables, or embeddings

Dimensionality reduction can be applied to various types of data, such as numerical, categorical, textual, image, audio, video, or mixed data. However, different types of data may require different types of dimensionality reduction algorithms or techniques.

There are many dimensionality reduction algorithms or techniques that have been developed and used for different purposes and scenarios. Some of the most popular and widely used dimensionality reduction algorithms are:

Principal component analysis (PCA): A linear and unsupervised algorithm that finds the orthogonal directions or components that capture the maximum variance or information in the data
Linear discriminant analysis (LDA): A linear and supervised algorithm that finds the directions or components that maximize the separation or discrimination between different classes or groups in the data
T-distributed stochastic neighbor embedding (t-SNE): A nonlinear and unsupervised algorithm that finds a low-dimensional embedding of the data that preserves the local neighborhood or similarity structure of the data

Matlab provides many functions and tools that can help you perform dimensionality reduction on your data using these algorithms. For example, you can use the pca function to perform principal component analysis, the fitcdiscr function to perform linear discriminant analysis, and the tsne function to perform t-distributed stochastic neighbor embedding.

Here is an example of how you can perform principal component analysis on the sample dataset ‘fisheriris’ that you loaded and visualized in the previous subsection:

% Load the sample dataset 'fisheriris' that contains measurements of iris flowers
load fisheriris

% Convert the data to a table and add variable names
data = array2table(meas,'VariableNames',{'SepalLength','SepalWidth','PetalLength','PetalWidth'});

% Add the species name as a categorical variable
data.Species = categorical(species);

% Perform principal component analysis on the numerical variables
[coeff,score,latent,explained] = pca(data(:,1:4));

% Display the principal component coefficients
coeff

% Display the percentage of variance explained by each principal component
explained

% Plot the first two principal components, colored by species
figure
scatter(score(:,1),score(:,2),[],data.Species)
xlabel('PC1')
ylabel('PC2')
title('Principal Component Analysis of Iris Data')
legend('setosa','versicolor','virginica')

As you will see, the principal component analysis algorithm has found four principal components that are linear combinations of the original variables. The principal component coefficients show the weights or contributions of each variable to each component. The percentage of variance explained shows how much information or variation each component captures from the data. The scatter plot shows the projection of the data onto the first two principal components, which explain about 95% of the total variance. You can also see that the principal components separate the different species well, indicating that they capture the relevant features of the data.

By performing dimensionality reduction on your data, you can reduce the complexity and size of your data, as well as find a lower-dimensional representation of your data that can preserve its essential characteristics or structure.

In the next subsection, you will learn how to perform anomaly detection on your data using Matlab.

3.4. Anomaly Detection Algorithms

Anomaly detection is a type of unsupervised learning that aims to identify outliers or anomalies in the data. Outliers or anomalies are data points that deviate significantly from the normal or expected behavior of the data. Anomaly detection can be useful for many applications, such as:

Detecting fraud or malicious activities in transactions, network traffic, or user behavior
Detecting faults or errors in machines, systems, or processes
Detecting rare or novel events or patterns in data streams, images, or text

Anomaly detection can be challenging for several reasons, such as:

The definition of normal or expected behavior may vary depending on the context or domain
The anomalies may be very rare, subtle, or complex to detect
The data may be high-dimensional, noisy, or incomplete

There are different types of anomaly detection algorithms or techniques, depending on the nature of the data and the problem. Some of the most common types are:

One-class classification: This type of anomaly detection treats the problem as a binary classification task, where the normal data points belong to one class and the anomalies belong to another class. However, unlike regular classification, only the normal class is available for training, and the anomalies are unknown or unlabeled. The goal is to learn a boundary or a model that can separate the normal class from the anomalies. Examples of one-class classification algorithms are one-class support vector machines, one-class random forests, and autoencoders.
Density-based clustering: This type of anomaly detection treats the problem as a clustering task, where the data points are grouped based on their density or similarity. The assumption is that the normal data points form dense clusters, while the anomalies are isolated or sparse. The goal is to identify the clusters and the outliers based on some criteria or threshold. Examples of density-based clustering algorithms are local outlier factor, DBSCAN, and isolation forest.
Distance-based or nearest-neighbor: This type of anomaly detection treats the problem as a distance or similarity measurement task, where the data points are compared based on their distance or similarity to other data points. The assumption is that the normal data points are close or similar to their neighbors, while the anomalies are far or dissimilar. The goal is to calculate the distance or similarity scores and rank the data points based on their outlier scores. Examples of distance-based or nearest-neighbor algorithms are k-nearest neighbors, Mahalanobis distance, and cosine similarity.

In this section, you will learn how to implement and compare some of the most popular anomaly detection algorithms in Matlab, using the Statistics and Machine Learning Toolbox and the Deep Learning Toolbox. You will use a synthetic dataset that contains two features and two classes: normal and anomalous. You will also learn how to evaluate the performance of your anomaly detection models using different metrics and techniques.

4. How to Evaluate Unsupervised Learning Models in Matlab

After you have trained your unsupervised learning models in Matlab, you may want to evaluate their performance and compare their results. However, unlike supervised learning, where you can use the labels or targets to measure the accuracy or error of your models, unsupervised learning does not have a clear or objective way to evaluate the models. Therefore, you need to use different metrics and techniques to assess the quality and validity of your unsupervised learning models.

In this section, you will learn how to use some of the most common metrics and techniques to evaluate your unsupervised learning models in Matlab, such as:

Silhouette analysis: This is a graphical technique that measures how well each data point fits into its assigned cluster. It calculates a silhouette coefficient for each data point, which ranges from -1 to 1, where a higher value indicates a better fit. You can use the silhouette function in Matlab to compute and plot the silhouette coefficients for your clustering models.
Elbow method: This is a heuristic technique that helps you determine the optimal number of clusters for your data. It plots the sum of squared distances (SSE) of the data points to their cluster centroids against the number of clusters, and looks for a point where the SSE curve bends or elbows. You can use the evalclusters function in Matlab to perform the elbow method for your clustering models.
Confusion matrix: This is a table that shows the distribution of the predicted and actual labels of your data. It can help you measure the agreement or disagreement between your unsupervised learning models and the ground truth labels, if available. You can use the confusionmat function in Matlab to create a confusion matrix for your unsupervised learning models.
Receiver operating characteristic (ROC) curve: This is a graphical technique that plots the true positive rate (TPR) against the false positive rate (FPR) of your models at different threshold levels. It can help you measure the sensitivity and specificity of your anomaly detection models, and compare their performance. You can use the perfcurve function in Matlab to generate and plot the ROC curves for your anomaly detection models.

By using these metrics and techniques, you will be able to evaluate your unsupervised learning models in Matlab and choose the best ones for your data and problem. In the next section, you will learn how to visualize your unsupervised learning models and their results using different techniques and tools in Matlab.

4.1. Validation Metrics

Validation metrics are numerical measures that quantify the quality and validity of your unsupervised learning models. They can help you compare different models and choose the best one for your data and problem. However, validation metrics are not always easy to interpret or apply, as they may depend on various factors, such as the type of data, the type of model, the type of task, and the type of metric.

Some of the most common validation metrics for unsupervised learning are:

Sum of squared distances (SSE): This metric measures the total squared distance of the data points to their cluster centroids. It indicates how compact or cohesive the clusters are. A lower SSE value means a better clustering model. However, SSE tends to decrease as the number of clusters increases, so it is not a good metric to determine the optimal number of clusters.
Davies-Bouldin index (DBI): This metric measures the ratio of the within-cluster scatter to the between-cluster separation. It indicates how well-separated and well-formed the clusters are. A lower DBI value means a better clustering model. However, DBI may not work well for clusters with different shapes or densities.
Calinski-Harabasz index (CHI): This metric measures the ratio of the between-cluster variance to the within-cluster variance. It indicates how distinct and homogeneous the clusters are. A higher CHI value means a better clustering model. However, CHI may favor models with more clusters, so it is not a good metric to determine the optimal number of clusters.
Silhouette coefficient (SC): This metric measures the average similarity of a data point to its own cluster compared to the nearest cluster. It indicates how well each data point fits into its assigned cluster. The SC ranges from -1 to 1, where a higher value means a better fit. The average SC of all data points can be used as a global measure of the clustering model. However, SC may not work well for clusters with different sizes or densities.
Area under the curve (AUC): This metric measures the area under the receiver operating characteristic (ROC) curve, which plots the true positive rate (TPR) against the false positive rate (FPR) of a model at different threshold levels. It indicates how well the model can discriminate between the normal and anomalous data points. The AUC ranges from 0 to 1, where a higher value means a better anomaly detection model. However, AUC may not be sensitive to the distribution or proportion of the anomalies in the data.

In Matlab, you can use different functions and tools to calculate and compare these validation metrics for your unsupervised learning models. In the next subsections, you will learn how to use some of these functions and tools for your clustering and anomaly detection models.

4.2. Visualization Techniques

Visualization techniques are graphical methods that help you display and explore your data and your unsupervised learning models. They can help you understand the structure and patterns of your data, the results and performance of your models, and the relationships and interactions between the data and the models. Visualization techniques can also help you communicate and present your findings and insights to others in an effective and appealing way.

Some of the most common visualization techniques for unsupervised learning are:

Scatter plot: This is a simple and widely used technique that plots the data points in a two-dimensional space based on their values or features. It can help you see the distribution and shape of the data, the outliers or anomalies, and the clusters or groups of the data. You can use different colors, shapes, or sizes to represent different attributes or labels of the data. You can use the scatter or gscatter functions in Matlab to create scatter plots for your data.
Heat map: This is a technique that uses colors to represent the values or intensities of a matrix or a table. It can help you see the correlations or associations between the variables or features of the data, the similarities or dissimilarities between the data points, and the patterns or trends of the data. You can use different color schemes or gradients to represent different ranges or scales of the values. You can use the heatmap or imagesc functions in Matlab to create heat maps for your data.
Biplot: This is a technique that combines a scatter plot and a loading plot to represent both the data points and the variables or features of the data in a two-dimensional space. It can help you see the relationships and interactions between the data points and the variables, the contributions or influences of the variables to the data, and the clusters or groups of the data and the variables. You can use different vectors or arrows to represent the directions or magnitudes of the variables. You can use the biplot function in Matlab to create biplots for your data.
Contour plot: This is a technique that uses curves or lines to represent the contours or boundaries of a surface or a function. It can help you see the regions or areas of the data that have different values or levels of the surface or the function, the gradients or changes of the values or levels, and the peaks or valleys of the surface or the function. You can use different colors or labels to represent different values or levels of the contours. You can use the contour or contourf functions in Matlab to create contour plots for your data.

In Matlab, you can use different functions and tools to create and customize these visualization techniques for your unsupervised learning models. In the next subsections, you will learn how to use some of these functions and tools for your clustering and dimensionality reduction models.

5. Conclusion

In this blog, you have learned how to perform unsupervised learning in Matlab, a popular programming language and platform for numerical computing and data analysis. You have learned how to:

Prepare and visualize your data for unsupervised learning
Train and compare different clustering algorithms, such as k-means, hierarchical clustering, and Gaussian mixture models
Train and compare different dimensionality reduction algorithms, such as principal component analysis, linear discriminant analysis, and t-distributed stochastic neighbor embedding
Train and compare different anomaly detection algorithms, such as one-class support vector machines, local outlier factor, and isolation forest
Evaluate and visualize your unsupervised learning models using different metrics and techniques, such as silhouette analysis, elbow method, confusion matrix, and ROC curve

By following this blog, you have gained a solid understanding of the basics of unsupervised learning and how to apply it to your own data using Matlab. You have also seen how Matlab can provide you with many built-in functions and toolboxes that can help you implement and evaluate unsupervised learning models.

Unsupervised learning is a powerful and versatile branch of machine learning that can help you discover the hidden features or characteristics of your data without any guidance or supervision. It can be useful for many purposes, such as data exploration, data reduction, data segmentation, data detection, and data generation. Unsupervised learning can also be applied to various types of data, such as numerical, categorical, textual, image, audio, video, or mixed data.

We hope you have enjoyed this blog and learned something new and useful. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy learning!