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Synopsis
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.
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About the Author
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.
From the Back Cover
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 [Paperback] Fu, Yun and Ma, Yunqian (eng)
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers theoretical analysis and real-world applications for graph embedding Examines subspace analysis with L1 graph Describes graph-based inference on Riemannian manifolds for visual analysisDr. Yun Fu is a professor at the State U. Seller Inventory # 4213082
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Graph Embedding for Pattern Analysis
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Springer New York Dez 2014, 2014
ISBN 10: 1489990623
ISBN 13: 9781489990624
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 268 pp. Englisch. Seller Inventory # 9781489990624
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Graph Embedding for Pattern Analysis
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9781489990624
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Graph Embedding for Pattern Analysis
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ISBN 10: 1489990623
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Condition: 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: 268 pages, 45 black & white tables, biography. BIC Classification: TJK; TTBM; UYQ; UYQP. Category: (G) General (US: Trade). Dimension: 235 x 155 x 14. Weight in Grams: 415. . 2013. Paperback. . . . . Seller Inventory # V9781489990624
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