Sciweavers

Share
CVPR
2011
IEEE

A Non-convex Relaxation Approach to Sparse Dictionary Learning

7 years 10 months ago
A Non-convex Relaxation Approach to Sparse Dictionary Learning
Dictionary learning is a challenging theme in computer vision. The basic goal is to learn a sparse representation from an overcomplete basis set. Most existing approaches employ a convex relaxation scheme to tackle this challenge due to the strong ability of convexity in computation and theoretical analysis. In this paper we propose a non-convex online approach for dictionary learning. To achieve the sparseness, our approach treats a so-called minimax concave (MC) penalty as a nonconvex relaxation of the 0 penalty. This treatment expects to obtain a more robust and sparse representation than existing convex approaches. In addition, we employ an online algorithm to adaptively learn the dictionary, which makes the non-convex formulation computationally feasible. Experimental results on the sparseness comparison and the applications in image denoising and image inpainting demonstrate that our approach is more effective and flexible.
Jianping Shi, Xiang Ren, Jingdong Wang, Guang Dai,
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where CVPR
Authors Jianping Shi, Xiang Ren, Jingdong Wang, Guang Dai, Zhihua Zhang
Comments (0)
books