A safe sample screening rule for Laplacian twin parametric-margin support vector machine

作者：

Highlights：

• A safe sample screening rule (SSSR-LTPSVM) for the LTPSVM is presented based on variational inequality.

• SSSR-LTPSVM can detect support vectors before solving optimization problems and delete the redundant samples for solving the quadratic programming problems.

• SSSR-LTPSVM not only works efficiently but also guarantees the solutions to be exactly the same with the LTPSVM.

摘要

•A safe sample screening rule (SSSR-LTPSVM) for the LTPSVM is presented based on variational inequality.•SSSR-LTPSVM can detect support vectors before solving optimization problems and delete the redundant samples for solving the quadratic programming problems.•SSSR-LTPSVM not only works efficiently but also guarantees the solutions to be exactly the same with the LTPSVM.

论文关键词：Semi-supervised learning,Laplacian graph,Support vector machine,Safe screening

论文评审过程：Received 10 April 2017, Revised 13 May 2018, Accepted 25 June 2018, Available online 27 June 2018, Version of Record 4 July 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.06.018