LDA (Linear Discriminant Analysis) Classifier with MATLAB Implementation

This article presents MATLAB implementations of both LDA (Linear Discriminant Analysis) classifier and PCA (Principal Component Analysis) techniques for data classification and dimensionality reduction. LDA classifier is a supervised machine learning algorithm that leverages known class labels to project data into a subspace maximizing class separability, enabling effective classification of new data points. PCA serves as an unsupervised dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving maximum variance, facilitating improved data visualization and analysis. In our MATLAB implementation, the LDA algorithm involves computing within-class and between-class scatter matrices, followed by eigenvalue decomposition to determine the optimal projection vectors. The classification process utilizes these projections to assign new samples to appropriate classes based on distance metrics. Meanwhile, our PCA implementation employs covariance matrix computation and singular value decomposition to identify principal components that capture the most significant data variations. By applying both techniques to our dataset, we achieved meaningful insights: LDA demonstrated superior classification performance by explicitly modeling class separability, while PCA provided enhanced data visualization capabilities through effective dimensionality reduction. The MATLAB code incorporates functions for data preprocessing, scatter matrix calculations, eigenvalue analysis, and projection visualization, offering a comprehensive framework for comparative analysis of these fundamental machine learning techniques.