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

Semi-Supervised Regression using Cluster Ensemble and Low-Rank Co-Association Matrix Decomposition under Uncertainties