Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26934
Title: Sparse tensor discriminant analysis
Authors: Lai, Z
Xu, Y
Yang, J
Tang, J
Zhang, D 
Keywords: Face recognition
Feature extraction
Linear discriminant analysis
Sparse projections
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2013, v. 22, no. 10, 6518139, p. 3904-3915 How to cite?
Journal: IEEE transactions on image processing 
Abstract: The classical linear discriminant analysis has undergone great development and has recently been extended to different cases. In this paper, a novel discriminant subspace learning method called sparse tensor discriminant analysis (STDA) is proposed, which further extends the recently presented multilinear discriminant analysis to a sparse case. Through introducing the L1 and L2 norms into the objective function of STDA, we can obtain multiple interrelated sparse discriminant subspaces for feature extraction. As there are no closed-form solutions, k-mode optimization technique and the L 1 norm sparse regression are combined to iteratively learn the optimal sparse discriminant subspace along different modes of the tensors. Moreover, each non-zero element in each subspace is selected from the most important variables/factors, and thus STDA has the potential to perform better than other discriminant subspace methods. Extensive experiments on face databases (Yale, FERET, and CMU PIE face databases) and the Weizmann action database show that the proposed STDA algorithm demonstrates the most competitive performance against the compared tensor-based methods, particularly in small sample sizes.
URI: http://hdl.handle.net/10397/26934
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2013.2264678
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