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Journal Articles Pattern Recognition Year : 2006

Gaussian fields for semi-supervised regression and correspondence learning

Jakob Verbeek
Nikos Vlassis
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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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Dates and versions

inria-00321133 , version 1 (16-02-2011)

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