Dimensionality Reduction and Feature Selection

This book, titled "A Comprehensive Guide to Dimensionality Reduction and Feature Selection" by Bouchene Mohammed Mehdi, presents a structured mathematical and practical approach to managing high-dimensional data using Python.The book is organized into three primary sections:1. Foundations: Analyzes the geometric and statistical challenges of high-dimensional spaces (the "Curse of Dimensionality"), establishes mathematical preliminaries in linear algebra and information theory, and details classical linear projection methods such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).2. Manifold Learning: Explores non-linear dimensionality reduction techniques designed to preserve local and global structures in curved data manifolds, covering Isomap, Locally Linear Embedding (LLE), t-SNE, and UMAP.3. Feature Selection: Examines methodologies to isolate relevant variables, systematically categorizing them into filter, wrapper, and embedded methods (such as Lasso and tree-based techniques).The book concludes with a practical MLOps workflow roadmap and mathematical derivations.