Multiple kernel-based dictionary learning for weakly supervised classification

In this paper, we develop a multiple instance learning (MIL) algorithm using the dictionary learning framework where the labels are given in the form of positive and negative bags, with each bag containing multiple samples. A positive bag is guaranteed to have only one positive class sample while all the samples in a negative bag belong to the negative class. Given positive and negative bags of data, our method learns appropriate feature space to select positive samples from the positive bags as well as optimal dictionaries to represent data in these bags. We apply this method for digit recognition, action recognition, and gender recognition tasks and demonstrate that the proposed method is robust and can perform significantly better than many competitive two class MIL classification algorithms.