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03352cam a2200433Ia 4500 |
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99883532904151 |
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20241030105327.0 |
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151127s2016 nyua b 001 0 eng d |
| 010 |
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|a 2015958763
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|a 9780387878102
|q (hbk)
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|a 0387878106
|q (hbk)
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| 020 |
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|a 9780387878119
|q (eBook)
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|a 0387878114
|q (eBook)
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| 035 |
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|a (OCoLC)911210592
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| 035 |
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|a (OCoLC)ocn911210592
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| 040 |
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|a YDXCP
|c YDXCP
|d DLC
|d MUU
|d OCLCO
|d U@S
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| 042 |
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|a lccopycat
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| 049 |
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|a U@SA
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| 050 |
0 |
0 |
|a QA300
|b .V47 2016
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| 082 |
0 |
0 |
|a 515/.9
|2 23
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| 100 |
1 |
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|a Vidal, René.
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| 245 |
1 |
0 |
|a Generalized principal component analysis /
|c René Vidal, Yi Ma, Shankar Sastry.
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| 260 |
|
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|a New York :
|b Springer,
|c c2016.
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| 300 |
|
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|a xxxii, 566 p. :
|b il. ;
|c 24 cm.
|
| 490 |
1 |
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|a Interdisciplinary applied mathematics ;
|v 40
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| 504 |
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|a Incluye referencias bibliográficas (p. 535-552) e índice.
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| 505 |
0 |
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|a Preface -- 1. Introduction -- Part I. Modeling data with single subspace: 2. Principal component analysis -- 3. Robust principal component analysis -- 4. Nonlinear and nonparametric extensions -- Part II. Modeling data with multiple subspaces: 5. Algebraic-geometric methods -- 6. Statistical methods -- 7. Spectral methods -- 8. Sparse and low-rank methods -- Part III. Applications: 9. Image representation -- 10. Image segmentation -- 11. Motion segmentation -- 12. Hybrid system identification -- Final words -- Appendix A. Basic facts from optimization -- Appendix B. Basic facts from mathematical statistics -- Appendix C. Basic facts from algebraic geometry.
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| 520 |
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|a "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." --Descripción del editor.
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| 650 |
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0 |
|a Mathematical analysis
|v Textbooks.
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| 650 |
|
0 |
|a Image processing
|x Mathematics
|v Textbooks.
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| 650 |
|
0 |
|a Big data
|v Textbooks.
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| 650 |
|
0 |
|a Manifolds (Mathematics)
|v Textbooks.
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| 650 |
|
7 |
|a Análisis matemático
|v Libros de texto.
|2 UDESA
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| 650 |
|
7 |
|a Procesamiento de imágenes
|x Matemáticas
|v Libros de texto.
|2 UDESA
|
| 650 |
|
7 |
|a Grandes volúmenes de datos
|v Libros de texto.
|2 UDESA
|
| 650 |
|
7 |
|a Variedades (Matemáticas)
|v Libros de texto.
|2 UDESA
|
| 700 |
1 |
|
|a Ma, Yi.
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| 700 |
1 |
|
|a Sastry, S. S.
|c (Mathematician)
|
| 830 |
|
0 |
|a Interdisciplinary applied mathematics ;
|v v. 40.
|