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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Graph Embedding for Pattern Analysis

Graph Embedding for Pattern Analysis

Fu, Yun
Ma, Yunqian

Paperback

Technology & EngineeringGeneral Computers

ISBN10: 1489990623
ISBN13: 9781489990624
Publisher: Springer Nature
Published: Dec 13 2014
Pages: 260
Weight: 0.84
Height: 0.57 Width: 6.14 Depth: 9.21
Language: English
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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