Representative Selection with Structured Sparsity

作者：

Highlights：

• We propose a novel formulation to find representatives in data samples via learning with structured sparsity.

• The proposed structured sparsity learning encourages sparsity, diversity and locality-sensitivity in representative selection.

• An accelerated proximal gradient algorithm is given to optimize the proposed formulation.

摘要

Highlights•We propose a novel formulation to find representatives in data samples via learning with structured sparsity.•The proposed structured sparsity learning encourages sparsity, diversity and locality-sensitivity in representative selection.•An accelerated proximal gradient algorithm is given to optimize the proposed formulation.

论文关键词：Representative selection,Structured sparsity,Diversity

论文评审过程：Received 5 January 2016, Revised 11 October 2016, Accepted 12 October 2016, Available online 14 October 2016, Version of Record 20 October 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.10.014