Multi-view based unlabeled data selection using feature transformation methods for semiboost learning

Abstract

SemiBoost [23] is a boosting framework for semi-supervised learning, in which unlabeled data as well as labeled data both contribute to learning. Various strategies have been proposed in the literature to perform the task of selecting useful unlabeled data in SemiBoost. Recently, a multi-view based strategy was proposed in [20], in which the feature set of the data is decomposed into