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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Vidal, René (Autor), Ma, Yi (Autor), Sastry, Shankar (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2016.
Edición:1st ed. 2016.
Colección:Interdisciplinary Applied Mathematics, 40
Temas:
System theory.
Control theory.
Computer vision.
Signal processing.
Statistics .
Algebraic geometry.
Systems Theory, Control .
Computer Vision.
Signal, Speech and Image Processing .
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Algebraic Geometry.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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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