Semi-Supervised Detrended Correspondence Analysis Algorithm

Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. In this framework, This paper proposes a method working under a multi-view setting. we motivate BIC to optimize classifiers selection and use DCCA (Detrended Canonical Correspondence Analysis) to complete unlabeled examples selection by eliminating the arch effect. We empirically show that classification performance increases by improving the semi-supervised algorithm´s ability to correctly assign labels to previously-unlabelled data. Experiments validate the effectiveness of the proposed method.