Tensor linear Laplacian discrimination (TLLD) for feature extraction

Authors:
Wei Zhang;Zhouchen Lin;Xiaoou Tang
Affiliations:
Department of Information Engineering, The Chinese University of Hong Kong, Rm. 802, Ho Sin Hang Engineering Building, Shatin, Hong Kong;Microsoft Research Asia, Sigma Building, Zhichun Road #49, Haidian District, Beijing 100190, PR China;Department of Information Engineering, The Chinese University of Hong Kong, Rm. 802, Ho Sin Hang Engineering Building, Shatin, Hong Kong
Venue:
Pattern Recognition
Year:
2009

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Abstract

Discriminant feature extraction plays a central role in pattern recognition and classification. In this paper, we propose the tensor linear Laplacian discrimination (TLLD) algorithm for extracting discriminant features from tensor data. TLLD is an extension of linear discriminant analysis (LDA) and linear Laplacian discrimination (LLD) in directions of both nonlinear subspace learning and tensor representation. Based on the contextual distance, the weights for the within-class scatters and the between-class scatter can be determined to capture the principal structure of data clusters. This makes TLLD free from the metric of the sample space, which may not be known. Moreover, unlike LLD, the parameter tuning of TLLD is very easy. Experimental results on face recognition, texture classification and handwritten digit recognition show that TLLD is effective in extracting discriminative features.