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Robust Label Prediction via Label Propagation and Geodesic k-Nearest Neighbor in Online Semi-Supervised Learning
Yuichiro WADA Siqiang SU Wataru KUMAGAI Takafumi KANAMORI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2019/08/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
semi-supervised learning, label propagation, manifold learning, online learning, geodesic distance,
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This paper proposes a computationally efficient offline semi-supervised algorithm that yields a more accurate prediction than the label propagation algorithm, which is commonly used in online graph-based semi-supervised learning (SSL). Our proposed method is an offline method that is intended to assist online graph-based SSL algorithms. The efficacy of the tool in creating new learning algorithms of this type is demonstrated in numerical experiments.