Semi-supervised one-pass multi-view learning

Abstract

Multi-view learning machines aim to process multi-view data set which consists of instances with different views. In recent years, the scale of multi-view data set is more and more larger and traditional multi-view learning machines which should store the entire data set cannot process them with the limitation of computation and storage ability. In order to overcome such a disadvantage, one-pass multi-view (OPMV) framework has been proposed and it goes through instances only once without storing the entire data set. But most multi-view data sets are semi-supervised, i.e., some training instances of the data sets are labeled while others are unlabeled. Furthermore, for a real-world multi-view data set, labeling instances is a high-cost task. In this paper, we propose semi-supervised OPMV (SSOPMV) framework for solving this issue. In SSOPMV, we develop an approach to generate additional unlabeled instances which possess useful discriminant information and apply them into the original OPMV framework along with the labeled and original unlabeled training instances. We verify the effectiveness of the SSOPMV with some real-world semi-supervised multi-view data sets used theoretically and empirically.