Graph Embedding for Pattern Analysis
Yun Fu · Yunqian Ma
2012年11月 · Springer Science & Business Media
电子书
260
页
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关于此电子书
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.
作者简介
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.
Dr. Yunqian Ma is a senior principal research scientist of Honeywell Labs at the Honeywell International Inc.
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