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» The intrinsic dimensionality of graphs
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IJON
2010
121views more  IJON 2010»
13 years 7 months ago
Sample-dependent graph construction with application to dimensionality reduction
Graph construction plays a key role on learning algorithms based on graph Laplacian. However, the traditional graph construction approaches of -neighborhood and k-nearest-neighbor...
Bo Yang, Songcan Chen
ICML
2007
IEEE
14 years 10 months ago
Entire regularization paths for graph data
Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high ...
Koji Tsuda
NIPS
2001
13 years 11 months ago
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motiva...
Mikhail Belkin, Partha Niyogi
SDM
2008
SIAM
256views Data Mining» more  SDM 2008»
13 years 11 months ago
Graph Mining with Variational Dirichlet Process Mixture Models
Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high ...
Koji Tsuda, Kenichi Kurihara
PAMI
2007
148views more  PAMI 2007»
13 years 9 months ago
Orthogonal Neighborhood Preserving Projections: A Projection-Based Dimensionality Reduction Technique
This paper considers the problem of dimensionality reduction by orthogonal projection techniques. The main feature of the proposed techniques is that they attempt to preserve both...
Effrosini Kokiopoulou, Yousef Saad