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Semi-supervised Discriminant Analysis Using Adaptive Neighbor Selection and Low-rank Representation
Author(s): Shi Jun, Jiang Zhiguo, Zhao Danpei, Lu Ming
Pages: 238-
248
Year: 2015
Issue:
2
Journal: Journal of Computer-Aided Design & Computer Graphics
Keyword: graph embedding; low-rank representation; semi-supervised learning; dimensionality reduction; face recognition;
Abstract: Considering the discriminant projection methods based on graph embedding are sensitive to the neighbor parameter and the fact that there is no sufficient class-label information of samples in practical applica-tions which has an impact on the performance of graph embedding based methods, a semi-supervised discrimi-nant analysis method based on adaptive neighbor selection and low-rank representation is proposed. The method uses all the intraclass samples to construct the intraclass graph which can characterize the intraclass compactness, and simultaneously adaptively selects the interclass samples within the neighborhood produced by the farthest in-traclass sample to construct the interclass graph which is used to characterize the interclass separability. Further-more, the low-rank representation approach is applied to mine the latent low-rank structure of unlabeled samples and thus preserve the global similarity relationship of samples. Experimental results on ORL and FERET face da-tabases demonstrate the effectiveness of our method and the robustness to noise.
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