An Improved Laplacian Semi-Supervised Regression

In this paper, we present an improved approach for semi-supervised regression problems. Our proposal is based on both, the use of the top eigen functions of integral operator derived from both labeled and unlabeled examples as the basis functions; and the learning of the prediction function by a Laplacian regularized regression. We compare our method with some representative ones dealing with semi-supervised regression. This comparison is done over several public data sets. We also verify the effectiveness of the proposed algorithm to reconstitute the installation date of the pipes of the Lyon Metropolis sewer network.