Dictionary-based Multiple Instance Learning

We present a multi-class, multiple instance learning (MIL) algorithm using the dictionary learning framework where the data is given in the form of bags. Each bag contains multiple samples, called instances, out of which at least one belongs to the class of the bag. We propose a noisy-OR model-based optimization framework for learning the dictionaries. Our method can be viewed as a generalized dictionary learning algorithm since it reduces to a novel discriminative dictionary learning framework when there is only one instance in each bag. Various experiments using the popular MIL datasets show that the proposed method performs better than existing methods.