Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning

TL;DR

This paper introduces ELIMIPL, a novel algorithm that leverages conjugate label information and intrinsic label space properties to enhance multi-instance partial-label learning, outperforming existing methods on benchmark and real-world datasets.

Contribution

The paper proposes a new algorithm, ELIMIPL, which exploits conjugate label information and label space properties to improve disambiguation in MIPL tasks.

Findings

ELIMIPL outperforms existing MIPL algorithms.

Experimental results validate the effectiveness of ELIMIPL.

ELIMIPL demonstrates superior performance on real-world datasets.

Abstract

Multi-instance partial-label learning (MIPL) addresses scenarios where each training sample is represented as a multi-instance bag associated with a candidate label set containing one true label and several false positives. Existing MIPL algorithms have primarily focused on mapping multi-instance bags to candidate label sets for disambiguation, disregarding the intrinsic properties of the label space and the supervised information provided by non-candidate label sets. In this paper, we propose an algorithm named ELIMIPL, i.e., Exploiting conjugate Label Information for Multi-Instance Partial-Label learning, which exploits the conjugate label information to improve the disambiguation performance. To achieve this, we extract the label information embedded in both candidate and non-candidate label sets, incorporating the intrinsic properties of the label space. Experimental results obtained from benchmark and real-world datasets demonstrate the superiority of the proposed ELIMIPL over existing MIPL algorithms and other well-established partial-label learning algorithms.