l0 Norm Based Dictionary Learning by Proximal Methods with Global Convergence

Chenglong Bao, Hui Ji, Yuhui Quan, Zuowei Shen; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 3858-3865

Abstract


Sparse coding and dictionary learning have seen their applications in many vision tasks, which usually is formulated as a non-convex optimization problem. Many iterative methods have been proposed to tackle such an optimization problem. However, it remains an open problem to have a method that is not only practically fast but also is globally convergent. In this paper, we proposed a fast proximal method for solving l0 norm based dictionary learning problems, and we proved that the whole sequence generated by the proposed method converges to a stationary point with sub-linear convergence rate. The benefit of having a fast and convergent dictionary learning method is demonstrated in the applications of image recovery and face recognition.

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[bibtex]
@InProceedings{Bao_2014_CVPR,
author = {Bao, Chenglong and Ji, Hui and Quan, Yuhui and Shen, Zuowei},
title = {l0 Norm Based Dictionary Learning by Proximal Methods with Global Convergence},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}