Non-convex regularized self-representation for unsupervised feature selection

作者：

Highlights：

• A non-convex regularized self-representation model is proposed.

• An iterative reweighted least square algorithm is developed to solve the non-convex (0 < p < 1) case.

• An augmented Lagrange method is introduced to solve the non-convex and non-differentiable L20-norm regularized RSR model.

摘要

•A non-convex regularized self-representation model is proposed.•An iterative reweighted least square algorithm is developed to solve the non-convex (0 < p < 1) case.•An augmented Lagrange method is introduced to solve the non-convex and non-differentiable L20-norm regularized RSR model.

论文关键词：Self-representation,Unsupervised feature selection,Sparse representation,Group sparsity

论文评审过程：Received 2 May 2016, Revised 8 September 2016, Accepted 16 November 2016, Available online 24 November 2016, Version of Record 22 March 2017.

论文官网地址：https://doi.org/10.1016/j.imavis.2016.11.014