Semi-supervised learning with Gaussian fields

Abstract : Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. This paper presents two contributions. First, we show how the GF framework can be used for regression tasks on high-dimensional data. We consider an active learning strategy based on entropy minimization and a maximum likelihood model selection method. Second, we show how a recent generalization of the Locally Linear Embedding algorithm for correspondence learning can also be cast into the GF framework, which obviates the need to choose a representation dimensionality.
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https://hal.inria.fr/inria-00321480
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Jakob Verbeek, Nikos Vlassis. Semi-supervised learning with Gaussian fields. [Technical Report] IAS-UVA-05, 2005, pp.20. ⟨inria-00321480v2⟩

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