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

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Wei Liu, Junfeng He, Shih-Fu Chang
• Keywords: Anchor Points, Graph Construction, Graph Laplacians, ICML 2010, Machine Learning