|
Activity Number:
|
31
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
| Abstract - #304389 |
|
Title:
|
Empirical Stationary Correlations for Semi-Supervised Learning on Graphs
|
|
Author(s):
|
Ya Xu*+
|
|
Companies:
|
Stanford University
|
|
Address:
|
74 Barnes Ct. #218, Stanford, CA, 94305,
|
|
Keywords:
|
semi-supervised learning ; graph ; kriging ; empirical covariance ; stationary correlation ; variogram
|
|
Abstract:
|
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. We prove that many such proposals are equivalent to kriging predictors based on a 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.
|
