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...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York ; Heidelberg ; Dordrecht ; London
Springer
[2016]
|
| Schriftenreihe: | Interdisciplinary applied mathematics
volume 40 |
| Schlagworte: | |
| 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. |
| Beschreibung: | Literaturangaben Seite 535-552 |
| Beschreibung: | xxxii, 566 Seiten Illustrationen, Diagramme, Portraits (überwiegend farbig) |
| ISBN: | 9780387878102 0387878106 9781493979127 |
Internformat
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| 264 | 1 | |a New York ; Heidelberg ; Dordrecht ; London |b Springer |c [2016] | |
| 264 | 4 | |c © 2016 | |
| 300 | |a xxxii, 566 Seiten |b Illustrationen, Diagramme, Portraits (überwiegend farbig) | ||
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| 490 | 1 | |a Interdisciplinary applied mathematics |v volume 40 | |
| 500 | |a Literaturangaben Seite 535-552 | ||
| 520 | |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. | ||
| 650 | 4 | |a Mathematical analysis |v Textbooks | |
| 650 | 4 | |a Image processing |x Mathematics |v Textbooks | |
| 650 | 4 | |a Big data |v Textbooks | |
| 650 | 4 | |a Manifolds (Mathematics) |v Textbooks | |
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| 650 | 4 | |a Image processing | |
| 650 | 4 | |a Manifolds (Mathematics) | |
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Datensatz im Suchindex
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| author | Vidal, René Ma, Yi Sastry, Shankar 1956- |
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| author_facet | Vidal, René Ma, Yi Sastry, Shankar 1956- |
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| bvnumber | BV044603336 |
| callnumber-first | Q - Science |
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| ctrlnum | (OCoLC)950461104 (DE-599)BVBBV044603336 |
| dewey-full | 515/.9 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 515 - Analysis |
| dewey-raw | 515/.9 |
| dewey-search | 515/.9 |
| dewey-sort | 3515 19 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Book |
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| id | DE-604.BV044603336 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:57:09Z |
| institution | BVB |
| isbn | 9780387878102 0387878106 9781493979127 |
| language | English |
| lccn | 015958763 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030001718 |
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| owner_facet | DE-92 DE-11 DE-83 DE-188 |
| physical | xxxii, 566 Seiten Illustrationen, Diagramme, Portraits (überwiegend farbig) |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Springer |
| record_format | marc |
| series | Interdisciplinary applied mathematics |
| series2 | Interdisciplinary applied mathematics |
| spelling | Vidal, René (DE-588)1112677461 aut Generalized principal component analysis René Vidal, Yi Ma, S. Shankar Sastry New York ; Heidelberg ; Dordrecht ; London Springer [2016] © 2016 xxxii, 566 Seiten Illustrationen, Diagramme, Portraits (überwiegend farbig) txt rdacontent n rdamedia nc rdacarrier Interdisciplinary applied mathematics volume 40 Literaturangaben Seite 535-552 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. Mathematical analysis Textbooks Image processing Mathematics Textbooks Big data Textbooks Manifolds (Mathematics) Textbooks Big data Image processing Manifolds (Mathematics) Mathematical analysis Hauptkomponentenanalyse (DE-588)4129174-8 gnd rswk-swf Hauptkomponentenanalyse (DE-588)4129174-8 s DE-604 Ma, Yi (DE-588)142301809 aut Sastry, Shankar 1956- (DE-588)121291057 aut Erscheint auch als Online-Ausgabe 978-0-387-87811-9 Interdisciplinary applied mathematics volume 40 (DE-604)BV004216726 40 |
| spellingShingle | Vidal, René Ma, Yi Sastry, Shankar 1956- Generalized principal component analysis Interdisciplinary applied mathematics Mathematical analysis Textbooks Image processing Mathematics Textbooks Big data Textbooks Manifolds (Mathematics) Textbooks Big data Image processing Manifolds (Mathematics) Mathematical analysis Hauptkomponentenanalyse (DE-588)4129174-8 gnd |
| subject_GND | (DE-588)4129174-8 |
| title | Generalized principal component analysis |
| title_auth | Generalized principal component analysis |
| title_exact_search | Generalized principal component analysis |
| title_full | Generalized principal component analysis René Vidal, Yi Ma, S. Shankar Sastry |
| title_fullStr | Generalized principal component analysis René Vidal, Yi Ma, S. Shankar Sastry |
| title_full_unstemmed | Generalized principal component analysis René Vidal, Yi Ma, S. Shankar Sastry |
| title_short | Generalized principal component analysis |
| title_sort | generalized principal component analysis |
| topic | Mathematical analysis Textbooks Image processing Mathematics Textbooks Big data Textbooks Manifolds (Mathematics) Textbooks Big data Image processing Manifolds (Mathematics) Mathematical analysis Hauptkomponentenanalyse (DE-588)4129174-8 gnd |
| topic_facet | Mathematical analysis Textbooks Image processing Mathematics Textbooks Big data Textbooks Manifolds (Mathematics) Textbooks Big data Image processing Manifolds (Mathematics) Mathematical analysis Hauptkomponentenanalyse |
| volume_link | (DE-604)BV004216726 |
| work_keys_str_mv | AT vidalrene generalizedprincipalcomponentanalysis AT mayi generalizedprincipalcomponentanalysis AT sastryshankar generalizedprincipalcomponentanalysis |