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Generalized Principal Component Analysis

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 challen...

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Bibliographic Details
Main Authors: Vidal, René, Ma, Yi (Author), Sastry, Shankar (Author)
Format: eBook
Language:English
Published: New York, NY Springer New York 2016, 2016
Edition:1st ed. 2016
Series:Interdisciplinary Applied Mathematics
Subjects:
Algebraic Geometry
Computer Vision
Statistics 
Control Theory
Systems Theory, Control
System Theory
Signal, Speech And Image Processing
Statistics In Engineering, Physics, Computer Science, Chemistry And Earth Sciences
Signal Processing
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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

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