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By
Yunqian Ma (editor) and
Yun Fu (editor)
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Bibliographic Detail
Publisher
Springer Verlag
Publication date
November 17, 2012
Pages
260
Binding
Hardcover
Book category
Adult Non-Fiction
ISBN-13
9781461444565
ISBN-10
146144456X
Dimensions
0.75 by 6.25 by 9 in.
Weight
1.15 lbs.
Original list price
$149.00
Other format details
sci/tech
Summaries and Reviews
Amazon.com description: Product Description: 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.
Editions
Hardcover
The price comparison is for this edition
With Yun Fu (other contributor) |
from Springer Verlag (November 17, 2012)
9781461444565 | details & prices | 260 pages | 6.25 × 9.00 × 0.75 in. | 1.15 lbs | List price $149.00
About: Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision.
About: Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision.
Paperback
With Yun Fu (other contributor) |
from Springer Verlag (December 13, 2014)
9781489990624 | details & prices | List price $149.00
About: Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision.
About: Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision.
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