Robust semi-supervised least squares classification by implicit constraints

Jesse H. Krijthe, Marco Loog

Abstract: 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.