Robust semi-supervised least squares classification by implicit constraints

作者：

Highlights：

• A novel convex formulation for robust semi-supervised learning using squared loss.

• A proof that this procedure never reduces performance in terms of the squared loss for the 1-dimensional case without intercept.

• An empirical evaluation of the properties of this classifier.

摘要

Highlights•A novel convex formulation for robust semi-supervised learning using squared loss.•A proof that this procedure never reduces performance in terms of the squared loss for the 1-dimensional case without intercept.•An empirical evaluation of the properties of this classifier.

论文关键词：Semi-supervised learning,Robust,Least squares classification

论文评审过程：Received 14 December 2015, Revised 12 July 2016, Accepted 18 September 2016, Available online 20 September 2016, Version of Record 30 September 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.009