Active Learning in Incomplete Label Multiple Instance Multiple Label Learning

TL;DR

This paper introduces a novel active learning method for incomplete-label multiple instance multiple label learning, utilizing a discriminative graphical model and a bag-class pair selection strategy to reduce labeling costs while maintaining performance.

Contribution

It proposes a new active learning approach tailored for incomplete-label MIML using a graphical model and an innovative query strategy, addressing the scarcity of existing methods.

Findings

The approach is robust on benchmark datasets.

It achieves effective classification with fewer labeled samples.

The method offers efficient model updates via stochastic gradient descent.

Abstract

In multiple instance multiple label learning, each sample, a bag, consists of multiple instances. To alleviate labeling complexity, each sample is associated with a set of bag-level labels leaving instances within the bag unlabeled. This setting is more convenient and natural for representing complicated objects, which have multiple semantic meanings. Compared to single instance labeling, this approach allows for labeling larger datasets at an equivalent labeling cost. However, for sufficiently large datasets, labeling all bags may become prohibitively costly. Active learning uses an iterative labeling and retraining approach aiming to provide reasonable classification performance using a small number of labeled samples. To our knowledge, only a few works in the area of active learning in the MIML setting are available. These approaches can provide practical solutions to reduce labeling cost but their efficacy remains unclear. In this paper, we propose a novel bag-class pair based approach for active learning in the MIML setting. Due to the partial availability of bag-level labels, we focus on the incomplete-label MIML setting for the proposed active learning approach. Our approach is based on a discriminative graphical model with efficient and exact inference. For the query process, we adapt active learning criteria to the novel bag-class pair selection strategy. Additionally, we introduce an online stochastic gradient descent algorithm to provide an efficient model update after each query. Numerical experiments on benchmark datasets illustrate the robustness of the proposed approach.