Robust Label Prediction via Label Propagation and Geodesic k-Nearest Neighbor in Online Semi-Supervised Learning

Yuichiro WADA  Siqiang SU  Wataru KUMAGAI  Takafumi KANAMORI  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.8   pp.1537-1545
Publication Date: 2019/08/01
Publicized: 2019/04/26
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7424
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
Keyword: 
semi-supervised learning,  label propagation,  manifold learning,  online learning,  geodesic distance,  

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Summary: 
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.