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...
Συγγραφή απο Οργανισμό/Αρχή: | |
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Άλλοι συγγραφείς: | , |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
New York, NY :
Springer New York : Imprint: Springer,
2013.
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Θέματα: | |
Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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 ℓ1-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.