Feature Extraction and Data Dimensionality Reduction Using MATLAB

Performing feature extraction and data dimensionality reduction on raw data using MATLAB is a critical task in data preprocessing and analysis. For feature extraction, various algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) can be implemented through MATLAB's built-in functions like pca() and fitcdiscr() to capture essential information from datasets. Code implementation typically involves normalizing input data, computing covariance matrices, and extracting eigenvectors to transform features into a lower-dimensional space. Data dimensionality reduction further simplifies datasets while preserving meaningful patterns, employing methods like Singular Value Decomposition (SVD) via the svd() function and Locally Linear Embedding (LLE) using custom scripts or toolboxes. These techniques help visualize high-dimensional data and improve computational efficiency. Successful execution requires careful algorithm selection and parameter tuning—such as choosing the number of principal components in PCA or neighbors in LLE—to optimize results. MATLAB’s visualization tools (e.g., plot, scatter3) can then be used to validate outcomes and interpret reduced-dimensional data structures.