View Construction for Multi-view Semi-supervised Learning

Abstract

Recent developments on semi-supervised learning have witnessed the effectiveness of using multiple views, namely integrating multiple feature sets to design semi-supervised learning methods. However, the so-called multiview semi-supervised learning methods require the availability of multiple views. For many problems, there are no ready multiple views, and although the random split of the original feature sets can generate multiple views, it is definitely not the most effective approach for view construction. In this paper, we propose a feature selection approach to construct multiple views by means of genetic algorithms. Genetic algorithms are used to find promising feature subsets, two of which having maximum classification agreements are then retained as the best views constructed from the original feature set. Besides conducting experiments with single-task support vector machine (SVM) classifiers, we also apply multitask SVM classifiers to the multi-view semi-supervised learning problem. The experiments validate the effectiveness of the proposed view construction method.