Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61061
Title: Joint tensor feature analysis for visual object recognition
Authors: Wong, WK 
Lai, Z
Xu, Y
Wen, J
Ho, CP 
Keywords: Discriminant analysis
Feature selection
Object recognition
Sparse learning
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 2015, v. 45, no. 11, 6980062, p. 2425-2436 How to cite?
Journal: IEEE transactions on cybernetics 
Abstract: Tensor-based object recognition has been widely studied in the past several years. This paper focuses on the issue of joint feature selection from the tensor data and proposes a novel method called joint tensor feature analysis (JTFA) for tensor feature extraction and recognition. In order to obtain a set of jointly sparse projections for tensor feature extraction, we define the modified within-class tensor scatter value and the modified between-class tensor scatter value for regression. The k-mode optimization technique and the L2,1-norm jointly sparse regression are combined together to compute the optimal solutions. The convergent analysis, computational complexity analysis and the essence of the proposed method/model are also presented. It is interesting to show that the proposed method is very similar to singular value decomposition on the scatter matrix but with sparsity constraint on the right singular value matrix or eigen-decomposition on the scatter matrix with sparse manner. Experimental results on some tensor datasets indicate that JTFA outperforms some well-known tensor feature extraction and selection algorithms.
URI: http://hdl.handle.net/10397/61061
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2014.2374452
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