Simultaneous Sparse Dictionary Learning and Pruning

Simeng Qu; Xiao Wang

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arXiv · 1605.07870

Simultaneous Sparse Dictionary Learning and Pruning

Simeng Qu·Xiao Wang

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Abstract

Dictionary learning is a cutting-edge area in imaging processing, that has recently led to state-of-the-art results in many signal processing tasks. The idea is to conduct a linear decomposition of a signal using a few atoms of a learned and usually over-completed dictionary instead of a pre-defined basis. Determining a proper size of the to-be-learned dictionary is crucial for both precision and efficiency of the process, while most of the existing dictionary learning algorithms choose the size quite arbitrarily. In this paper, a novel regularization method called the Grouped Smoothly Clipped Absolute Deviation (GSCAD) is employed for learning the dictionary. The proposed method can simultaneously learn a sparse dictionary and select the appropriate dictionary size. Efficient algorithm is designed based on the alternative direction method of multipliers (ADMM) which decomposes the joint non-convex problem with the non-convex penalty into two convex optimization problems. Several examples are presented for image denoising and the experimental results are compared with other state-of-the-art approaches.

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BibTeX RIS

Simeng Qu, Xiao Wang. 2016-05-25. Simultaneous Sparse Dictionary Learning and Pruning. https://arxiv.org/abs/1605.07870

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