Adaptive p-posterior mixture-model kernels for multiple instance learning

In multiple instance learning (MIL), how the instances determine the bag-labels is an essential issue, both algorithmically and intrinsically. In this paper, we show that the mechanism of how the instances determine the bag-labels is different for different application domains, and does not necessarily obey the traditional assumptions of MIL. We therefore propose an adaptive framework for MIL that adapts to different application domains by learning the domain-specific mechanisms merely from labeled bags. Our approach is especially attractive when we are encountered with novel application domains, for which the mechanisms may be different and unknown. Specifically, we exploit mixture models to represent the composition of each bag and an adaptable kernel function to represent the relationship between the bags. We validate on synthetic MIL datasets that the kernel function automatically adapts to different mechanisms of how the instances determine the bag-labels. We also compare our approach with state-of-the-art MIL techniques on real-world benchmark datasets.