Graph Embedding for Pattern Analysis -
-25% koodilla BOOKS
Toimitus 17-23 arkipäivässä
30 päivän palautusoikeus
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 ... Täydellinen kuvaus
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Loppu
Kuvaus
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.
Lisätietoja
| Julkaisija | Springer New York |
|---|---|
| Julkaisuvuosi | 2012 |
| Kannen tyyppi | Kovakantinen |
| EAN | 9781461444565 |