PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gaussian fields for semi-supervised regression and correspondence learning
Jakob Verbeek and Nikos Vlassis
Pattern Recognition Volume 39, Number 10, pp. 1864-1875, 2006.


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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:4219
Deposited By:Jakob Verbeek
Deposited On:04 December 2008