Label Propagation with Weak Supervision

Rattana Pukdee, Dylan Sam, Pradeep Kumar Ravikumar, Nina Balcan

Keywords: Semi-supervised learning, Weakly supervised learning, Label propagation

TL;DR: Theoretical analysis of label propagation with prior information and connection to weak supervision

Abstract: Semi-supervised learning and weakly supervised learning are important paradigms that aim to reduce the growing demand for labeled data in current machine learning applications. In this paper, we introduce a novel analysis of the classical label propagation algorithm (LPA) (Zhu & Ghahramani, 2002) that moreover takes advantage of useful prior information, specifically probabilistic hypothesized labels on the unlabeled data. We provide an error bound that exploits both the local geometric properties of the underlying graph and the quality of the prior information. We also propose a framework to incorporate multiple sources of noisy information. In particular, we consider the setting of weak supervision, where our sources of information are weak labelers. We demonstrate the ability of our approach on multiple benchmark weakly supervised classification tasks, showing improvements upon existing semi-supervised and weakly supervised methods.

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Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2210.03594/code)