  In this paper, we solve a semi-supervised regression problem. Due to the lack
of knowledge about the data structure and the presence of random noise, the
considered data model is uncertain. We propose a method which combines graph
Laplacian regularization and cluster ensemble methodologies. The co-association
matrix of the ensemble is calculated on both labeled and unlabeled data; this
matrix is used as a similarity matrix in the regularization framework to derive
the predicted outputs. We use the low-rank decomposition of the co-association
matrix to significantly speedup calculations and reduce memory. Numerical
experiments using the Monte Carlo approach demonstrate robustness, efficiency,
and scalability of the proposed method.
