EMPIRICAL STATIONARY CORRELATIONS FOR SEMI-SUPERVISED LEARNING ON GRAPHS

• 成果类型: Article
• 署名作者: Xu, Ya; Dyer, Justin S.; Owen, Art B.
• 署名单位: Stanford University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/09-AOAS293
• 发表日期: 2010
• 页码: 589-614
• 关键词: regularization; network

摘要

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.