How can a sparse representation be made applicable for very low-dimensional data?

作者：

Highlights：

• We analyze the problem of sparse representation on low-dimensional data.

• We extend applicable scope of sparse representations via a novel perspective.

• An effective method to double the dimensionality is proposed for classification.

摘要

•We analyze the problem of sparse representation on low-dimensional data.•We extend applicable scope of sparse representations via a novel perspective.•An effective method to double the dimensionality is proposed for classification.

论文关键词：Sparse representation,Low dimension,Face recognition

论文评审过程：Received 21 May 2016, Revised 27 November 2016, Accepted 25 January 2017, Available online 1 February 2017, Version of Record 10 February 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.01.039