Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better represen

Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dime

This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applie

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, opti

This book on Principal Component Analysis (PCA) extensively explores the core analyses and case studies within this field, incorporating the latest advancements