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Graph Embedding for Pattern Analysis

Produktform: Buch / Einband - fest (Hardcover)

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.weiterlesen

Sprache(n): Englisch

ISBN: 978-1-4614-4456-5 / 978-1461444565 / 9781461444565

Verlag: Springer US

Erscheinungsdatum: 17.11.2012

Seiten: 260

Auflage: 1

Herausgegeben von Yun Fu, Yunqian Ma

106,99 € inkl. MwSt.
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