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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 graphbased 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. key words: semi-supervised learning, label propagation, manifold learning, online learning, geodesic distance

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