Sparse Support Matrix Machine

作者：

Highlights：

• We propose a novel matrix classifier to simultaneously leverage the structural information within matrices and select useful features.

• We regularize the combination of nuclear norm and l1 norm of the regression matrix and develop an efficient solver based on GFB splitting framework.

• We also provide a theoretical guarantee for the global convergence and analyze the excess risk statistically.

• We extensively evaluate the proposed SSMM on four real datasets. The results show that SSMM achieves competitive performance.

摘要

•We propose a novel matrix classifier to simultaneously leverage the structural information within matrices and select useful features.•We regularize the combination of nuclear norm and l1 norm of the regression matrix and develop an efficient solver based on GFB splitting framework.•We also provide a theoretical guarantee for the global convergence and analyze the excess risk statistically.•We extensively evaluate the proposed SSMM on four real datasets. The results show that SSMM achieves competitive performance.

论文关键词：Classification,Support vector machine,Matrix analysis,Sparse,Low rank

论文评审过程：Received 10 April 2017, Revised 27 September 2017, Accepted 6 October 2017, Available online 9 October 2017, Version of Record 8 January 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.10.003