A new self-paced method for multiple instance boosting learning

Abstract

Multi-instance learning is a useful tool for solving label ambiguity. MILBoost is one of the algorithms, which uses boosting method to handle the multiple instance learning problems. Although MILBoosting has achieved good effect on multiple instance learning, little work has been done on the problem of multiple instance learning where a small number of bags are labeled. In this paper, we propose a new approach by incorporating the SPL and boosting into the procedure of multiple instance learning, called Self-Paced Boost Multiple Instance Learning (SP-B-MIL). The proposed approach can improve the effectiveness and robustness of multi-instance learning when a small number of bags are labeled. We first reformulate the multiple instance boosting model with a self-paced loss formulation. Then we propose a self-paced function for realizing desired self-paced scheme, which makes it possible to select instances from different bags during each iteration. Finally, we design a simple and effective algorithm to solve the optimization problem. Experimental results show that the proposed algorithm is comparable to the classical algorithms in some multi-instance learning benchmark data sets.