Generalized principal component analysis [electronic resource] / René Vidal, Yi Ma, S.S. Sastry.
2016
QA300
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Can lend chapters, not whole ebooks
Details
Title
Generalized principal component analysis [electronic resource] / René Vidal, Yi Ma, S.S. Sastry.
Author
Vidal, René, author.
ISBN
9780387878119 (electronic book)
0387878114 (electronic book)
9780387878102
0387878114 (electronic book)
9780387878102
Published
New York, NY : Springer, 2016.
Language
English
Description
1 online resource (xxxii, 566 pages) : illustrations.
Other Standard Identifiers
10.1007/978-0-387-87811-9 doi
Call Number
QA300
Dewey Decimal Classification
515
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed April 19, 2016).
Added Author
Ma, Yi, author.
Sastry, S. S., author.
Sastry, S. S., author.
Series
Interdisciplinary applied mathematics ; v. 40. 0939-6047
Available in Other Form
Print version: 9780387878102
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Table of Contents
Preface
Acknowledgments
Glossary of Notation.- Introduction.- I Modeling Data with Single Subspace
Principal Component Analysis
Robust Principal Component Analysis
Nonlinear and Nonparametric Extensions
II Modeling Data with Multiple Subspaces
Algebraic-Geometric Methods
Statistical Methods
Spectral Methods
Sparse and Low-Rank Methods
III Applications
Image Representation
Image Segmentation
Motion Segmentation
Hybrid System Identification
Final Words
Appendices
References
Index.
Acknowledgments
Glossary of Notation.- Introduction.- I Modeling Data with Single Subspace
Principal Component Analysis
Robust Principal Component Analysis
Nonlinear and Nonparametric Extensions
II Modeling Data with Multiple Subspaces
Algebraic-Geometric Methods
Statistical Methods
Spectral Methods
Sparse and Low-Rank Methods
III Applications
Image Representation
Image Segmentation
Motion Segmentation
Hybrid System Identification
Final Words
Appendices
References
Index.