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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| Hauptverfasser: | , , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York ; Heidelberg ; Dordrecht ; London
Springer
[2016]
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| Schriftenreihe: | Interdisciplinary applied mathematics
volume 40 |
| Schlagworte: | |
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| Zusammenfassung: | 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. |
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| Beschreibung: | Literaturangaben Seite 535-552 |
| Beschreibung: | xxxii, 566 Seiten Illustrationen, Diagramme, Portraits (überwiegend farbig) |
| ISBN: | 9780387878102 0387878106 9781493979127 |