A similarity-based two-view multiple instance learning method for classification

Abstract

Multiple instance learning (MIL) has been proposed to classify the bag of instances. In practice, we may meet the problems which have more than one view data. For example, in the image classification, textual information is always used to describe the image, which can be considered as two-view data. In this paper, we propose a new similarity-based two-view multi-instance learning (STMIL) method that can incorporate two-view data into learning so as to improve classification accuracy of MIL. In order to obtain the predictive classifier, we first convert the proposed model into a convex optimization problem, and then propose a new alternative framework to solve the proposed method. We then analyze the convergence of the proposed STMIL method. The experiments have been conducted to compare the performance of our proposed method and the previous methods. The results show that our method can deliver superior performance than other methods.