Bag dissimilarity regularized multi-instance learning

Abstract

Multi-instance learning (MIL) is able to cope with the weakly supervised problems where the training data is represented by labeled bags consisting of multiple unlabeled instances. Due to its practical significance, MIL has recently drawn increasing attention. Introducing bag representations is an attractive way to learn MIL data. However, it is difficult for the existing MIL methods to utilize both implicit and explicit bag representations simultaneously. In this paper, we propose a bag dissimilarity regularized (BDR) framework that incorporates multiple bag representations regardless of explicitness or implicitness. Here, the implicit bag representations are incorporated into a regularization term that contains the intrinsic geometric information provided by the bag dissimilarities. The regularization term can be added to the objective function of supervised classifiers. An effective method for explicit bag embedding is also proposed, which exploits the Fisher score derived from factor analysis. Finally, we propose two specific BDR methods based on support vector machine and broad learning system. The proposed BDR methods are evaluated on 14 datasets, and have achieved competitive results with limited computation consumption. We also discuss the effectiveness and the characteristics of BDR framework.