Reference : Gaussian fields for semi-supervised regression and correspondence learning
Scientific journals : Article
Engineering, computing & technology : Computer science
Gaussian fields for semi-supervised regression and correspondence learning
Verbeek, Jakob J. [> >]
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Pattern Recognition
Pergamon Press - An Imprint of Elsevier Science
Yes (verified by ORBilu)
[en] Gaussian fields ; regression ; active learning ; model selection
[en] 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.

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