Parreto Principal

Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques, it continues to be the subject of much research, ranging from new model-based approaches to algorithmic ideas from neural networks. It is extremely versatile, with applications in many disciplines.
The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. Its length is nearly double that of the first edition.

Learn to apply Principal Component Analysis in Python from a Data Science expert. Code templates included.

Learn to apply Principal Component Analysis in Python from a Data Science expert. Code templates included.

Learn to apply Principal Component Analysis in Python from a Data Science expert. Code templates included.

This volume on principal component analysis (PCA) explores the core analyses and case studies within this field, incorporating the latest advancements. The book emphasizes the integration of PCA with other algorithms and methodologies, highlighting the enhancements achieved through combined approaches.

This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image and video segmentation, face recognition and clustering, and hybrid system identification etc.

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