Faster Kriging on Graphs

A Kriging procedure for processes indexed by graphs

Empirical stationary correlations for semi-supervised learning on graphs

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 estim...

EMPIRICAL STATIONARY CORRELATIONS FOR SEMI-SUPERVISED LEARNING ON GRAPHS By

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 covariance matrix driven by the link structure of the graph. We then propose a data-driven estimator o...

Semi-supervised Learning on Graphs – a Statistical Approach a Dissertation Submitted to the Department of Statistics and the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

Data on graphs are growing tremendously in size and prevalence these days; consider the World Wide Web graph or the Facebook social network. In semi-supervised learning on graphs, features observed at one node are used to estimate missing values at other nodes. Many prediction methods have been proposed in the machine learning community over the past few years. In this thesis we show that sever...

Kriging in a Parallel Environment

In spatial data modelling and analysis there are a variety of techniques to perform prediction. The goal of these techniques is to take spatially located data and to establish estimates of data values at unknown locations. Of these techniques, the attractive aspects of kriging are often overshadowed by the slow speed of the calculation. Unfortunately the calculations necessary to perform krigin...