Gaussian fields for semi-supervised regression and correspondence learning

Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. In this paper we show how the GF framework can be used for semi-supervised regression on high-dimensional data. We propose an active learning strategy based on entropy minimization and a maximum likelihood model selection method. Furthermore, we show how a recent generalization of the LLE algorithm for correspondence learning can be cast into the GF framework, which obviates the need to choose a representation dimensionality. 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

• Type: Journal
• Year: 2006
• Where: PR
• Authors: Jakob J. Verbeek, Nikos A. Vlassis
• Keywords: GF Framework, Maximum Likelihood Model, PR 2006, Semi-supervised Regression