Graph Embedding for Pattern Analysis - Brossura

Brossura

ISBN 10: 1489990623 ISBN 13: 9781489990624

Casa editrice: Springer, 2014

Riassunto

This book presents 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, and describes their real-world applications.

Su questo libro

Informazioni sull?autore

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.

Dalla quarta di copertina

Graph Embedding for Pattern Analysis 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.

Dettagli dell'edizione

• Editore: Springer
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 1489990623
• ISBN 13: 9781489990624
• Rilegatura: Copertina flessibile
• Numero di pagine: 268
• Redattore: Fu Yun, Ma Yunqian
• Contatto del produttore: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Germania