Probabilistic Decoupling of Labels in Classification

• URL: http://arxiv.org/abs/2006.09046v1
• Date: Tue, 16 Jun 2020 10:07:50 GMT
• Title: Probabilistic Decoupling of Labels in Classification
• Authors: Jeppe N{\o}rregaard and Lars Kai Hansen
• Abstract summary: We develop a principled, probabilistic, unified approach to non-standard classification tasks. We train a classifier on the given labels to predict the label-distribution. We then infer the underlying class-distributions by variationally optimizing a model of label-class transitions.
• Score: 4.865747672937677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we develop a principled, probabilistic, unified approach to non-standard classification tasks, such as semi-supervised, positive-unlabelled, multi-positive-unlabelled and noisy-label learning. We train a classifier on the given labels to predict the label-distribution. We then infer the underlying class-distributions by variationally optimizing a model of label-class transitions.

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