Dissimilarity-based multiple instance learning

Abstract

In this paper, we propose to solve multiple instance learning problems using a dissimilarity representation of the objects. Once the dissimilarity space has been constructed, the problem is turned into a standard supervised learning problem that can be solved with a general purpose supervised classifier. This approach is less restrictive than kernel-based approaches and therefore allows for the usage of a wider range of proximity measures. Two conceptually different types of dissimilarity measures are considered: one based on point set distance measures and one based on the earth movers distance between distributions of within- and between set point distances, thereby taking relations within and between sets into account. Experiments on five publicly available data sets show competitive performance in terms of classification accuracy compared to previously published results. © 2010 Springer-Verlag Berlin Heidelberg.