Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27891
Title: A generalized iterated shrinkage algorithm for non-convex sparse coding
Authors: Zuo, W
Meng, D
Zhang, L 
Feng, X
Zhang, D 
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings of the IEEE International Conference on Computer Vision, 2013, p. 217-224 How to cite?
Abstract: In many sparse coding based image restoration and image classification problems, using non-convex ℓp-norm minimization (0 ≤p <1) can often obtain better results than the convex ℓ1-norm minimization. A number of algorithms, e.g., iteratively reweighted least squares (IRLS), iteratively thresholding method (ITM-ℓp), and look-up table (LUT), have been proposed for non-convex ℓp-norm sparse coding, while some analytic solutions have been suggested for some specific values of p. In this paper, by extending the popular soft-thresholding operator, we propose a generalized iterated shrinkage algorithm (GISA) for ℓp-norm non-convex sparse coding. Unlike the analytic solutions, the proposed GISA algorithm is easy to implement, and can be adopted for solving non-convex sparse coding problems with arbitrary p values. Compared with LUT, GISA is more general and does not need to compute and store the look-up tables. Compared with IRLS and ITM-ℓp, GISA is theoretically more solid and can achieve more accurate solutions. Experiments on image restoration and sparse coding based face recognition are conducted to validate the performance of GISA.
Description: 2013 14th IEEE International Conference on Computer Vision, ICCV 2013, Sydney, NSW, 1-8 December 2013
URI: http://hdl.handle.net/10397/27891
ISBN: 9781479928392
DOI: 10.1109/ICCV.2013.34
Appears in Collections:Conference Paper

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