Multiple instance learning via bag space construction and ELM

Since the labels of training samples are related to bags not instances, the multiple instance learning (MIL) is a special ambiguous learning paradigm. In this paper, we propose a novel bag space (BS) construction and extreme learning machine (ELM) combination method named BS_ELM for MIL, which can capture the bag structure and use the efficiency of ELM. Firstly, sparse subspace clustering is performed to obtain the cluster centers and a new bag space is constructed. Then ELM is used to classify bags in the new space. Experiments on data sets demonstrate the utility and efficiency of the proposed approach as compared to the other state-of-the-art MIL algorithms.