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...
Κύριοι συγγραφείς: | , , |
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Συγγραφή απο Οργανισμό/Αρχή: | |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
New York, NY :
Springer New York : Imprint: Springer,
2016.
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Σειρά: | Interdisciplinary Applied Mathematics,
40 |
Θέματα: | |
Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.