Dissimilarity-Based Multiple Instance Learning

Abstract

Multiple instance learning (MIL) is an extension of supervised learning where the objects are represented by sets (bags) of feature vectors (instances) rather than individual feature vectors. For example, an image can be represented by a bag of instances, where each instance is a patch in that image. Only bag labels are given, however, the standard assumption is that that a bag is positive if and only if it contains a positive, or concept instance. In other words, only concept instances are informative for the bag label. The goal is to learn a bag classifier, although an instance classifier may also be desired. This scenario is suitable for applications where objects are heterogeneous and representing them as a single feature vector may lose important information, and/or in cases where only weakly labeled data is available. Several approaches to MIL exist. Instance-based approaches rely on stronger assumptions about the relationship of the instance labels and the bag labels, and define a bag classifier through an instance classifier. Bag-based approaches learn a bag classifier directly, often by converting the problem into a supervised problem. These methods often disregard the standard assumption, and instead use the collective assumption, where all instances are informative. One way to convert the problem into a supervised one, is to describe each bag by a vector of its distances to a set of reference prototypes. In this so-called dissimilarity representation, supervised classifiers can be used. The goal of this thesis is to study the dissimilarity representation as a method for dealing with multiple instance learning problems. We address the questions of defining a dissimilarity function and choosing a reference set of prototypes, while considering the assumptions that these choices implicitly make about the problem.