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

arXiv: https://arxiv.org/abs/1901.03919 · HTML: https://mlsanity.com/paper/1901.03919/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [Solving weakly supervised regression problem using low-rank manifold regularization](https://mlsanity.com/paper/2104.06548/) (2021)
- [Built Year Prediction from Buddha Face with Heterogeneous Labels](https://mlsanity.com/paper/2109.00812/) (2021)

## Abstract (arXiv)

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.