Dissimilarity-based multi-instance learning using dictionary learning and sparse coding ensembles

In multi-instance learning problems, samples are represented by multisets, which are named as bags. Each bag includes a set of feature vectors called instances. This differs multi-instance learning problems from classical supervised learning problems. In this paper, to convert a multi-instance learning problem into a supervised learning problem, fixed-size feature vectors of bags are computed using a dissimilarity based method. Then, dictionary learning based bagging and random subspace ensemble classification models are proposed to exploit the underlying discriminative structure of the dissimilarity based features. Experimental results are obtained on 11 different datasets from different multi-instance learning problem domains. It is shown that the proposed random subspace based dictionary ensemble algorithm gives the best results on 8 datasets in terms of classification accuracy and area under curve.