Gaussian fields for semi-supervised regression and correspondence learning
作者:
Highlights:
•
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.
论文关键词:Gaussian fields,Regression,Active learning,Model selection
论文评审过程:Received 10 February 2006, Accepted 6 April 2006, Available online 6 June 2006.
论文官网地址:https://doi.org/10.1016/j.patcog.2006.04.011