Graph Embedding for Pattern Analysis

Graph Embedding for Pattern Analysis


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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.

Product Details

ISBN-13: 9781489990624
Publisher: Springer New York
Publication date: 12/13/2014
Edition description: 2013
Pages: 260
Product dimensions: 6.10(w) x 9.25(h) x 0.02(d)

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.

Table of Contents

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.-

Graph Embedding for Speaker Recognition.

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