Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy.

Zhang, M; Gao, Y; Sun, C; Blumenstein, M

Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy.

Zhang, M Gao, Y Sun, C Blumenstein, M

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2021, 30, pp. 150-162
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Zhang, M
dc.contributor.author	Gao, Y
dc.contributor.author	Sun, C
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.date.accessioned	2022-02-28T03:56:01Z
dc.date.available	2022-02-28T03:56:01Z
dc.date.issued	2021
dc.identifier.citation	IEEE Transactions on Image Processing, 2021, 30, pp. 150-162
dc.identifier.issn	1057-7149
dc.identifier.issn	1941-0042
dc.identifier.uri	http://hdl.handle.net/10453/154919
dc.description.abstract	Traditional tensor decomposition methods, e.g., two dimensional principal component analysis and two dimensional singular value decomposition, that minimize mean square errors, are sensitive to outliers. To overcome this problem, in this paper we propose a new robust tensor decomposition method using generalized correntropy criterion (Corr-Tensor). A Lagrange multiplier method is used to effectively optimize the generalized correntropy objective function in an iterative manner. The Corr-Tensor can effectively improve the robustness of tensor decomposition with the existence of outliers without introducing any extra computational cost. Experimental results demonstrated that the proposed method significantly reduces the reconstruction error on face reconstruction and improves the accuracies on handwritten digit recognition and facial image clustering.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Image Processing
dc.relation.isbasedon	10.1109/TIP.2020.3033151
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy.
dc.type	Journal Article
utslib.citation.volume	30
utslib.location.activity	United States
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-02-28T03:56:00Z
pubs.publication-status	Published
pubs.volume	30

Abstract:

Traditional tensor decomposition methods, e.g., two dimensional principal component analysis and two dimensional singular value decomposition, that minimize mean square errors, are sensitive to outliers. To overcome this problem, in this paper we propose a new robust tensor decomposition method using generalized correntropy criterion (Corr-Tensor). A Lagrange multiplier method is used to effectively optimize the generalized correntropy objective function in an iterative manner. The Corr-Tensor can effectively improve the robustness of tensor decomposition with the existence of outliers without introducing any extra computational cost. Experimental results demonstrated that the proposed method significantly reduces the reconstruction error on face reconstruction and improves the accuracies on handwritten digit recognition and facial image clustering.

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http://hdl.handle.net/10453/154919