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Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.
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Über die Autorin bzw. den Autor
Dr. Yun Fu is a professor at the State University of New York at Buffalo
Dr. Yunqian Ma is a senior principal research scientist of Honeywell Labs at the Honeywell International Inc.
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Graph Embedding for Pattern Analysis covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.
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Graph Embedding for Pattern Analysis
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Graph Embedding for Pattern Analysis
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Springer New York, Springer New York Nov 2012, 2012
ISBN 10: 146144456X
ISBN 13: 9781461444565
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Buch. Zustand: Neu. Neuware -Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 268 pp. Englisch. Artikel-Nr. 9781461444565
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Graph Embedding for Pattern Analysis
Verlag:
Springer-Verlag New York Inc., 2012
ISBN 10: 146144456X
ISBN 13: 9781461444565
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Zustand: New. This book presents advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph and graph in vector spaces, and describes their real-world applications. Editor(s): Fu, Yun; Ma, Yunqian. Num Pages: 260 pages, 45 black & white tables, biography. BIC Classification: TJK; TTBM; UYQP. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 20. Weight in Grams: 567. . 2012. Hardback. . . . . Books ship from the US and Ireland. Artikel-Nr. V9781461444565
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Graph Embedding for Pattern Analysis
Verlag:
Springer New York, Springer New York, 2012
ISBN 10: 146144456X
ISBN 13: 9781461444565
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Graph Embedding for Pattern Analysis covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field. Artikel-Nr. 9781461444565
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Graph Embedding for Pattern Analysis
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Hardcover. Zustand: Brand New. 2013 edition. 268 pages. 9.20x6.10x0.80 inches. In Stock. Artikel-Nr. x-146144456X
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