Graph-based Semi-Supervised Regression and Its Extensions

In this paper we present a graph-based semi-supervised method for solving regression problem. In our method, we first build an adjacent graph on all labeled and unlabeled data, and then incorporate the graph prior with the standard Gaussian process prior to infer the training model and prediction distribution for semi-supervised Gaussian process regression. Additionally, to further boost the learning performance, we employ a feedback algorithm to pick up the helpful prediction of unlabeled data for feeding back and re-training the model iteratively. Furthermore, we extend our semi-supervised method to a clustering regression framework to solve the computational problem of Gaussian process. Experimental results show that our work achieves encouraging results.