Mixture graph based semi-supervised dimensionality reduction

Authors:
G. X. Yu;H. Peng;J. Wei;Q. L. Ma
Affiliations:
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China 510006;School of Computer Science and Engineering, South China University of Technology, Guangzhou, China 510006;School of Computer Science and Engineering, South China University of Technology, Guangzhou, China 510006;School of Computer Science and Engineering, South China University of Technology, Guangzhou, China 510006
Venue:
Pattern Recognition and Image Analysis
Year:
2010

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Abstract

Graph structure is crucial to graph based dimensionality reduction. A mixture graph based semi-supervised dimensionality reduction (MGSSDR) method with pairwise constraints is proposed. MGSSDR first constructs multiple diverse graphs on different random subspaces of dataset, then it combines these graphs into a mixture graph and does dimensionality reduction on this mixture graph. MGSSDR can preserve the pairwise constraints and local structure of samples in the reduced subspace. Meanwhile, it is robust to noise and neighborhood size. Experimental results on facial images feature extraction demonstrate its effectiveness.