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 ![]() | |
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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