Semi-supervised classification via both label and side information

Abstract

As for the semi-supervised learning, both label and side information serve as pretty significant indicators for the classification. However, majority of the associated works only focus on one side of the road. In other words, either the label information or the side information is utilized instead of taking both of them into consideration simultaneously. To address the referred defect, we propose a graph-based semi-supervised learning (GSL) problem via building the intrinsic graph and the penalty graph upon both label and side information. To efficiently unravel the proposed GSL problem, a novel quadratic trace ratio (QTR) method is proposed based on solving the associated QTR problem, which is the equivalent counterpart of the GSL problem. Besides, a parameter-free similarity is further derived and utilized. Consequently, a novel semi-supervised classification (SC) algorithm can be summarized by virtue of the proposed GSL problem and QTR method.