Screen LibraryGraph embedding for pattern analysis /
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, gr...
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| Corporate Authors: | |
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| Group Author: | ; |
| Published: |
Springer,
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| Publisher Address: | New York, NY : |
| Publication Dates: | 2013. |
| Literature type: | eBook |
| Language: | English |
| Subjects: | |
| Online Access: |
http://dx.doi.org/10.1007/978-1-4614-4457-2 |
| Summary: |
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. |
| Carrier Form: | 1 online resource |
| ISBN: |
9781461444572 (electronic bk.) 1461444578 (electronic bk.) |
| Index Number: | TK7882 |
| CLC: | TP391.4 |
| Contents: |
Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces / Feature Grouping and Selection Over an Undirected Graph / Median Graph Computation by Means of Graph Embedding into Vector Spaces / Patch Alignment for Graph Embedding / Improving Classifications Through Graph Embeddings / Learning with ?-Graph for High Dimensional Data Analysis / Graph-Embedding Discriminant Analysis on Riemannian Manifolds for Visual Recognition / A Flexible and Effective Linearization Method for Subspace Learning / A Multi-graph Spectral Framework for Mining Multi-source Anomalies / Graph Embedding for Speaker Recognition / |