Abstract : 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.
https://hal.inria.fr/inria-00321133
Contributeur : Jakob Verbeek
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Soumis le : mercredi 16 février 2011 - 16:32:45
Dernière modification le : mercredi 29 novembre 2017 - 14:49:18
Document(s) archivé(s) le : mardi 6 novembre 2012 - 14:01:19
