# Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags

## TL;DR

This paper introduces a convex positive-unlabeled (PU) learning approach for multiple instance learning (MIL), enabling effective learning from positive and unlabeled bags with improved performance and lower computational costs.

## Contribution

It presents a novel convex PU learning framework specifically designed for MIL, addressing the challenge of limited labeled data in practical scenarios.

## Key findings

- Achieves better performance than existing PU-MIL methods
- Significantly reduces computational costs
- Effective in scenarios with limited labeled bags

## Abstract

Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as bags) are composed of sub-elements (referred to as instances) and only bag labels are available. MIL has a variety of applications such as content-based image retrieval, text categorization and medical diagnosis. Most of the previous work for MIL assume that the training bags are fully labeled. However, it is often difficult to obtain an enough number of labeled bags in practical situations, while many unlabeled bags are available. A learning framework called PU learning (positive and unlabeled learning) can address this problem. In this paper, we propose a convex PU learning method to solve an MIL problem. We experimentally show that the proposed method achieves better performance with significantly lower computational costs than an existing method for PU-MIL.