Abstract
Recent years have witnessed a surge of interest in graph-based semi-supervised learning. However, two of the major problems in graph-based semi-supervised learning are: (1) how to set the hyperparameter in the Gaussian similarity; and (2) how to make the algorithm scalable. In this article, we introduce a general framework for graphbased learning. First, we propose a method called linear neighborhood propagation, which can automatically construct the optimal graph. Then we introduce a novel multilevel scheme to make our algorithm scalable for large data sets. The applications of our algorithm to various real-world problems are also demonstrated.
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This work was presented in part at the 14th International Symposium on Artificial Life and Robotics, Oita, Japan, February 5–7, 2009
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Zhang, C., Wang, F. Graph-based semi-supervised learning. Artif Life Robotics 14, 445–448 (2009). https://doi.org/10.1007/s10015-009-0719-5
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DOI: https://doi.org/10.1007/s10015-009-0719-5