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Graph Embedding for Pattern Analysis
. Ed(S): Fu, Yun; Ma, Yunqian
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Description for Graph Embedding for Pattern Analysis
Paperback. 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. Editor(s): Fu, Yun; Ma, Yunqian. Num Pages: 268 pages, 45 black & white tables, biography. BIC Classification: TJK; TTBM; UYQ; UYQP. Category: (G) General (US: Trade). Dimension: 235 x 155 x 14. Weight in Grams: 415.
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.
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
Format
Paperback
Publication date
2013
Publisher
Springer-Verlag New York Inc. United States
Number of pages
268
Condition
New
Number of Pages
260
Place of Publication
New York, United States
ISBN
9781489990624
SKU
V9781489990624
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
About . Ed(S): Fu, Yun; Ma, Yunqian
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.
Reviews for Graph Embedding for Pattern Analysis
From the reviews: “The papers in this collection apply the methods elaborated in classical and algebraic graph theory to analyze patterns in various contexts. … the book will be easy for a researcher well versed in the theoretical fundamentals of the presented methods. … the editors have been able to structure the contents in an effective and interesting way. ... Read more