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portada Graph Embedding for Pattern Analysis
Type
Physical Book
Publisher
Author
Language
English
Pages
260
Format
Hardcover
Dimensions
23.4 x 16.0 x 2.0 cm
Weight
0.43 kg.
ISBN13
9781461444565
Categories

Graph Embedding for Pattern Analysis

Fu, Yun ; Ma, Yunqian (Author) · Springer · Hardcover

Graph Embedding for Pattern Analysis - Fu, Yun ; Ma, Yunqian

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Synopsis "Graph Embedding for Pattern Analysis"

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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