Graph Embedding for Pattern Analysis
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ISBN-13:
9781461444565
Veröffentl:
2012
Einband:
HC gerader Rücken kaschiert
Erscheinungsdatum:
17.11.2012
Seiten:
268
Autor:
Yunqian Ma
Gewicht:
570 g
Format:
241x160x18 mm
Sprache:
Englisch
Beschreibung:
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.
Covers theoretical analysis and real-world applications for graph embedding
Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces.- Feature Grouping and Selection over an Undirected Graph.- Median Graph Computation by Means of Graph Embedding into Vector Spaces.- Patch Alignment for Graph Embedding.- Feature Subspace Transformations for Enhancing K-Means Clustering.- Learning with l1-Graph for High Dimensional Data Analysis.- Graph-Embedding Discriminant Analysis on Riemannian Manifolds for Visual Recognition.- A Flexible and Effective Linearization Method for Subspace Learning.- A Multi-Graph Spectral Approach for Mining Multi-Source Anomalies.
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