Skip to Main content Skip to Navigation
New interface
Reports (Technical report)

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.
Document type :
Reports (Technical report)
Complete list of metadata

Cited literature [21 references]  Display  Hide  Download
Contributor : Jakob Verbeek Connect in order to contact the contributor
Submitted on : Tuesday, April 5, 2011 - 2:49:23 PM
Last modification on : Wednesday, October 26, 2022 - 8:14:01 AM
Long-term archiving on: : Wednesday, July 6, 2011 - 2:56:25 AM


Files produced by the author(s)


  • HAL Id : inria-00321480, version 2



Jakob Verbeek, Nikos Vlassis. Semi-supervised learning with Gaussian fields. [Technical Report] IAS-UVA-05, 2005, pp.20. ⟨inria-00321480v2⟩



Record views


Files downloads