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.
Pattern Recognit.