Generalized Principal Component Analysis 1st edition by Rene Vidal, Yi Ma, Shankar Sastry 0387878106 9780387878102

Generalized Principal Component Analysis 1st edition by Rene Vidal, Yi Ma, Shankar Sastry – Ebook PDF Instant Download/Delivery: 0387878106, 9780387878102

Full download Generalized Principal Component Analysis 1st edition after payment

Product details:

ISBN 10: 0387878106

ISBN 13: 9780387878102

Author: Rene Vidal, Yi Ma, Shankar Sastry

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. This book is intended to serve as a textbook for graduate students and beginning researchers in data science, machine learning, computer vision, image and signal processing, and systems theory. It contains ample illustrations, examples, and exercises and is made largely self-contained with three Appendices which survey basic concepts and principles from statistics, optimization, and algebraic-geometry used in this book. René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.

Generalized Principal Component Analysis 1st Table of contents:

1. Modeling Data with a Single Subspace

2. Principal Component Analysis

3. Robust Principal Component Analysis

4. Nonlinear and Nonparametric Extensions

2. Modeling Data with Multiple Subspaces

5. Algebraic-Geometric Methods

6. Statistical Methods

7. Spectral Methods

8. Sparse and Low-Rank Methods

3. Applications

9. Image Representation

10. Image Segmentation

11. Motion Segmentation

12. Hybrid System Identification

13. Final Words

People also search for Generalized Principal Component Analysis 1st: 

principal component analysis genetics

principal component analysis stanford

principal-component analysis

generalized principal component analysis