Bag Reference Vector for Multi-instance Learning

TL;DR

This paper introduces a novel multi-instance learning algorithm that describes each bag using reference-based feature vectors, leveraging similarity measures to better handle label ambiguity and improve performance.

Contribution

The proposed method uniquely compares bags via reference vectors and extends Hausdorff distance to effectively address instance label ambiguity in MIL.

Findings

Outperforms previous state-of-the-art methods significantly

Effective in benchmark and text categorization tasks

Handles label ambiguity better than existing approaches

Abstract

Multi-instance learning (MIL) has a wide range of applications due to its distinctive characteristics. Although many state-of-the-art algorithms have achieved decent performances, a plurality of existing methods solve the problem only in instance level rather than excavating relations among bags. In this paper, we propose an efficient algorithm to describe each bag by a corresponding feature vector via comparing it with other bags. In other words, the crucial information of a bag is extracted from the similarity between that bag and other reference bags. In addition, we apply extensions of Hausdorff distance to representing the similarity, to a certain extent, overcoming the key challenge of MIL problem, the ambiguity of instances' labels in positive bags. Experimental results on benchmarks and text categorization tasks show that the proposed method outperforms the previous state-of-the-art by a large margin.