Discriminative GoDec+ for Classification

Guo, K; Xu, X; Tao, D

Discriminative GoDec+ for Classification

Guo, K Xu, X Tao, D

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Signal Processing, 2017, 65 (13), pp. 3414 - 3429
Issue Date:: 2017-07-01

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Field	Value	Language
dc.contributor.author	Guo, K	en_US
dc.contributor.author	Xu, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2017-07-01	en_US
dc.identifier.citation	IEEE Transactions on Signal Processing, 2017, 65 (13), pp. 3414 - 3429	en_US
dc.identifier.issn	1053-587X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123823
dc.description.abstract	© 1991-2012 IEEE. GoDec+ is a robust low-rank representation model, which adopts correntropy to model noise and corruptions. To extend GoDec+ for classification, this paper proposes discriminative GoDec+ (D-GoDec+). In the model, each class is represented by a shared underlying subspace and a specific transformation matrix. Structural label information and the Fisher discrimination criterion are incorporated to model the reconstruction errors and coefficients. An efficient solution to D-GoDec+ is proposed based on half-quadratic optimization, and convergence of the solution is rigorously analyzed. Based on D-GoDec+, a simple but effective classification method is presented by combining the discriminability of reconstruction errors and coefficients. Through the use of transformation matrices, the classification method avoids complex encoding computation in the dictionary represented methods and thus is very efficient. Experimental results on face recognition, object classification, scene classification, and action recognition demonstrate the advantages of the proposed model.	en_US
dc.relation.ispartof	IEEE Transactions on Signal Processing	en_US
dc.relation.isbasedon	10.1109/TSP.2017.2684746	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Discriminative GoDec+ for Classification	en_US
dc.type	Journal Article
utslib.citation.volume	13	en_US
utslib.citation.volume	65	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	13	en_US
pubs.publication-status	Published	en_US
pubs.volume	65	en_US

Abstract:

© 1991-2012 IEEE. GoDec+ is a robust low-rank representation model, which adopts correntropy to model noise and corruptions. To extend GoDec+ for classification, this paper proposes discriminative GoDec+ (D-GoDec+). In the model, each class is represented by a shared underlying subspace and a specific transformation matrix. Structural label information and the Fisher discrimination criterion are incorporated to model the reconstruction errors and coefficients. An efficient solution to D-GoDec+ is proposed based on half-quadratic optimization, and convergence of the solution is rigorously analyzed. Based on D-GoDec+, a simple but effective classification method is presented by combining the discriminability of reconstruction errors and coefficients. Through the use of transformation matrices, the classification method avoids complex encoding computation in the dictionary represented methods and thus is very efficient. Experimental results on face recognition, object classification, scene classification, and action recognition demonstrate the advantages of the proposed model.

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http://hdl.handle.net/10453/123823