A generalized iterated shrinkage algorithm for non-convex sparse coding

Wangmeng Zuo, Deyu Meng, Lei Zhang, Xiangchu Feng, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

261 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013
Number of pages8
ISBN (Print)9781479928392
Publication statusPublished - 1 Jan 2013
Event2013 14th IEEE International Conference on Computer Vision, ICCV 2013 - Sydney, NSW, Australia
Duration: 1 Dec 20138 Dec 2013


Conference2013 14th IEEE International Conference on Computer Vision, ICCV 2013
CitySydney, NSW

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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