A Generalized Iterated Shrinkage Algorithm for Non-convex Sparse Coding

View Researcher's Other Codes

MATLAB code for the paper: “A Generalized Iterated Shrinkage Algorithm for Non-convex Sparse Coding”.

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Wangmeng Zuo, Deyu Meng, Lei Zhang, Xiangchu Feng, David Zhang
Journal/Conference Name 2013 International Conference on Computer Vision (ICCV 2013)
Paper Category
Paper 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., 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
Date of publication 2013
Code Programming Language MATLAB
Comment

Copyright Researcher 2021