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dc.contributor.authorIqbal, A
dc.contributor.authorSeghouane, A-K
dc.identifier.citationIqbal, A. & Seghouane, A. -K. (2019). A alpha-Divergence-Based Approach for Robust Dictionary Learning. IEEE Transactions on Image Processing, 28 (11),
dc.description.abstractIn this paper, a robust sequential dictionary learning (DL) algorithm is presented. The proposed algorithm is motivated from the maximum likelihood perspective on dictionary learning and its link to the minimization of the Kullback-Leibler divergence. It is obtained by using a robust loss function in the data fidelity term of the DL objective instead of the usual quadratic loss. The proposed robust loss function is derived from the α-divergence as an alternative to the Kullback-Leibler divergence, which leads to a quadratic loss. Compared to other robust approaches, the proposed loss has the advantage of belonging to class of redescending M-estimators, guaranteeing inference stability from large deviations from the Gaussian nominal noise model. The algorithm is obtained by solving a sequence of penalized rank-1 matrix approximation problems, where the ℓ 1 -norm is introduced as a penalty promoting sparsity and then using a block coordinate descent approach to estimate the unknowns. Performance comparison with similar robust DL algorithms on digit recognition, background removal, and gray-scale image denoising is performed highlighting efficacy of the proposed algorithm.
dc.publisherInstitute of Electrical and Electronics Engineers
dc.titleA alpha-Divergence-Based Approach for Robust Dictionary Learning
dc.typeJournal Article
melbourne.affiliation.departmentElectrical and Electronic Engineering
melbourne.affiliation.departmentSchool of Mathematics and Statistics
melbourne.source.titleIEEE Transactions on Image Processing
melbourne.contributor.authorSeghouane, Abd-Krim
melbourne.contributor.authorIqbal, Asif
melbourne.identifier.fundernameidAUST RESEARCH COUNCIL, FT130101394
melbourne.accessrightsThis item is embargoed and will be available on 2021-11-01

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