Gaussian fields for semi-supervised regression and correspondence learning

PATTERN RECOGNITION

卷:39

Article

Verbeek, Jakob J. ; Vlassis, Nikos

关键词: Gaussian fields;    regression;    active learning;    model selection;   

DOI  :  10.1016/j.patcog.2006.04.011

来源: Elsevier

【 摘 要 】

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. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

【 授权许可】

Free