| Reference : Gaussian fields for semi-supervised regression and correspondence learning |
| Scientific journals : Article | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/11041 | |||
| Gaussian fields for semi-supervised regression and correspondence learning | |
| English | |
| Verbeek, Jakob J. [> >] | |
Vlassis, Nikos  [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >] | |
| 2006 | |
| Pattern Recognition | |
| Pergamon Press - An Imprint of Elsevier Science | |
| 39 | |
| 10 | |
| 1864-1875 | |
| Yes (verified by ORBilu) | |
| 0031-3203 | |
| [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. | |
| http://hdl.handle.net/10993/11041 |
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