Semi-Supervised Learning on an Augmented Graph with Class Labels

In this paper, we propose a novel graph-based method for semi-supervised learning. Our method runs a diffusion-based affinity learning algorithm on an augmented graph consisting of not only the nodes of labeled and unlabeled data but also artificial nodes representing class labels. The learned affinities between unlabeled data and class labels are used for classification. Our method achieves superior results on many standard data sets.

Authors

• Nan Li 0021
• Longin Jan Latecki

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