A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels

• URL: http://arxiv.org/abs/2103.04685v1
• Date: Mon, 8 Mar 2021 11:46:02 GMT
• Title: A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels
• Authors: Daiki Tanaka, Daiki Ikami, and Kiyoharu Aizawa
• Abstract summary: This research presents a methodology that assigns initial pseudo-labels to unlabeled data which is used as noisy-labeled data, and trains a deep neural network using the noisy-labeled data. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods on several benchmark datasets.
• Score: 49.990938653249415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Positive-unlabeled learning refers to the process of training a binary classifier using only positive and unlabeled data. Although unlabeled data can contain positive data, all unlabeled data are regarded as negative data in existing positive-unlabeled learning methods, which resulting in diminishing performance. We provide a new perspective on this problem -- considering unlabeled data as noisy-labeled data, and introducing a new formulation of PU learning as a problem of joint optimization of noisy-labeled data. This research presents a methodology that assigns initial pseudo-labels to unlabeled data which is used as noisy-labeled data, and trains a deep neural network using the noisy-labeled data. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods on several benchmark datasets.

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