Fast learning from label proportions with small bags

TL;DR

This paper introduces a novel EM-based neural network approach for learning from label proportions in small bags, achieving faster convergence and comparable or improved accuracy over existing methods.

Contribution

The paper presents a new EM algorithm tailored for small bags in LLP, explicitly considering all label combinations for improved learning.

Findings

Faster convergence compared to existing LLP methods

Achieves comparable or better accuracy

Effective on image datasets

Abstract

In learning from label proportions (LLP), the instances are grouped into bags, and the task is to learn an instance classifier given relative class proportions in training bags. LLP is useful when obtaining individual instance labels is impossible or costly. In this work, we focus on the case of small bags, which allows to design an algorithm that explicitly considers all consistent instance label combinations. In particular, we propose an EM algorithm alternating between optimizing a general neural network instance classifier and incorporating bag-level annotations. Using two different image datasets, we experimentally compare this method with an approach based on normal approximation and two existing LLP methods. The results show that our approach converges faster to a comparable or better solution.