Block sparse representation for pattern classification: Theory, extensions and applications

作者：

Highlights：

• We give theoretical guarantees for the block sparse representation based classifier.

• A RBSRC (robust block sparse representation based classifier) framework is given.

• We devise an efficient half-quadratic optimization algorithm for RBSRC.

• Using RBSRC as a general platform, we propose three robust classifiers.

摘要

•We give theoretical guarantees for the block sparse representation based classifier.•A RBSRC (robust block sparse representation based classifier) framework is given.•We devise an efficient half-quadratic optimization algorithm for RBSRC.•Using RBSRC as a general platform, we propose three robust classifiers.

论文关键词：Representation based classifier,Block sparsity,Subspace,M-estimator

论文评审过程：Received 15 September 2017, Revised 10 October 2018, Accepted 17 November 2018, Available online 22 November 2018, Version of Record 23 November 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.11.026