Empirical stationary correlations for semi-supervised learning on graphs

Details Empirical stationary correlations for semi-supervised learning on graphs Empirical stationary correlations for semi-supervised learning on graphs

2010-11-08

arxiv.org/abs/1011.1766v1

Annals of Applied Statistics 2010, Vol. 4, No. 2, 589-614

Statistics

Applications

Published in at http://dx.doi.org/10.1214/09-AOAS293 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/09-AOAS293

In semi-supervised learning on graphs, response variables observed at one node are used to estimate missing values at other nodes. The methods exploit correlations between nearby nodes in the graph. In this paper we prove that many such proposals are equivalent to kriging predictors based on a fixed covariance matrix driven by the link structure of the graph. We then propose a data-driven estimator of the correlation structure that exploits patterns among the observed response values. By incorporating even a small fraction of observed covariation into the predictions, we are able to obtain much improved prediction on two graph data sets.

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