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Gaussian fields for semi-supervised regression and correspondence learning

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.
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https://hal.inria.fr/inria-00321133
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Submitted on : Wednesday, February 16, 2011 - 4:32:45 PM
Last modification on : Wednesday, November 29, 2017 - 2:49:18 PM
Long-term archiving on: : Tuesday, November 6, 2012 - 2:01:19 PM

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Jakob Verbeek, Nikos Vlassis. Gaussian fields for semi-supervised regression and correspondence learning. Pattern Recognition, Elsevier, 2006, 39, pp.1864 - 1875. ⟨10.1016/j.patcog.2006.04.011⟩. ⟨inria-00321133⟩

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