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Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags

Abstract · Apr 22, 2017 08:50 ·

bags tokyo multiple mil bag positive labels instances instance cs-lg

Arxiv Abstract

  • Han Bao
  • Tomoya Sakai
  • Issei Sato
  • Masashi Sugiyama

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.

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