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2022 JMLR JMLR 2022

Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective

Abstract

In this paper we analyze the graph-based approach to semi-supervised learning under a manifold assumption. We adopt a Bayesian perspective and demonstrate that, for a suitable choice of prior constructed with sufficiently many unlabeled data, the posterior contracts around the truth at a rate that is minimax optimal up to a logarithmic factor. Our theory covers both regression and classification. [abs] [ pdf ][ bib ] © JMLR 2022. (edit, beta)

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Authors

Daniel Sanz-Alonso , Ruiyi Yang

Topics

Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning Machine Learning > Learning Types > Semi-Supervised Learning Machine Learning > Optimization & Theory > Bayesian Inference

Keywords

semi-supervised learning bayesian nonparametrics graph-based learning posterior contraction manifold assumption
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