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ICML
2010
IEEE

Large Graph Construction for Scalable Semi-Supervised Learning

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Large Graph Construction for Scalable Semi-Supervised Learning
In this paper, we address the scalability issue plaguing graph-based semi-supervised learning via a small number of anchor points which adequately cover the entire point cloud. Critically, these anchor points enable nonparametric regression that predicts the label for each data point as a locally weighted average of the labels on anchor points. Because conventional graph construction is inefficient in large scale, we propose to construct a tractable large graph by coupling anchorbased label prediction and adjacency matrix design. Contrary to the Nystr
Wei Liu, Junfeng He, Shih-Fu Chang
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICML
Authors Wei Liu, Junfeng He, Shih-Fu Chang
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