Journal article

A alpha-Divergence-Based Approach for Robust Dictionary Learning

Asif Iqbal, Abd-Krim Seghouane

IEEE Transactions on Image Processing | Institute of Electrical and Electronics Engineers | Published : 2019


In 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 l..

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University of Melbourne Researchers


Awarded by Australian Research Council

Funding Acknowledgements

This work was supported in part by the Australian Research Council under Grant FT. 130101394. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Chun-Shien Lu.