Graph 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...
Guardat en:
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| Altres autors: | |
| Format: | Livre numérique |
| Idioma: | Anglais |
| Publicat: |
New York, NY :
Springer New York
2013.
Cham : Springer Nature |
| Accés en línia: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Graph Embedding for Pattern Analysis, Texte imprimé, 9781461444565 • Graph Embedding for Pattern Analysis, Texte imprimé, 9781461444589 • Graph Embedding for Pattern Analysis, edited by Yun Fu, Yunqian Ma., 2013, New York, Springer, 1 vol. (VIII-260 p.), 978-1-4899-9062-4 |
Taula de continguts:
- 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
- Feature Subspace Transformations for Enhancing K-Means Clustering
- Learning with 1-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 Approach for Mining Multi-Source Anomalies
- Graph Embedding for Speaker Recognition.