pca

Preface

Principal Component Analysis (PCA) is one of the most widely-used techniques in data analysis, machine learning, and pattern recognition. It provides an effective way to reduce the dimensionality of data while preserving its essential structure. By focusing on the directions of greatest variance, PCA enables better visualization, interpretation, and analysis of complex datasets.

This book delves into the theory and practical application of PCA with a focus on using Singular Value Decomposition (SVD) for efficient computation. While traditional PCA relies on eigendecomposition, SVD offers a more powerful and scalable approach, particularly when dealing with large or sparse datasets.

Through this book, you will explore both the mathematical foundations and the practical implications of SVD-based PCA. You will learn not only how to compute PCA but also how to interpret the results, choose the optimal number of components, and apply PCA to real-world problems such as dimensionality reduction, feature selection, and data visualization.

Whether you are a student, data scientist, or practitioner, this book provides a comprehensive guide to mastering PCA through SVD, offering clear explanations, code examples, and case studies to help you apply these techniques in your own work.

---