Mixture graph based semi-supervised dimensionality reduction

被引：5

作者：

Yu G.X. ^{[1
]}

Peng H. ^{[1
]}

Wei J. ^{[1
]}

Ma Q.L. ^{[1
]}

机构：

[1] School of Computer Science and Engineering, South China University of Technology

来源：

Pattern Recognition and Image Analysis | 2010年 / 20卷 / 04期

关键词：

Dimensionality reduction; Mixture graph; Noise; Pairwise constraints;

D O I：

10.1134/S1054661810040140

中图分类号：

学科分类号：

摘要：

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. © 2010 Pleiades Publishing, Ltd.

引用

页码：536 / 541

页数：5

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