Synopsis
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.
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