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.

翻译：在本文中,我们解决了一个半监督回归问题。由于对数据结构缺乏了解和随机噪音的存在,考虑过的数据模型是不确定的。我们提出了一种方法,将图解拉placian正规化和集集集共性方法结合起来。共同组合矩阵的计算方法既有标签数据,也有未标签数据;该矩阵用作正规化框架中的类似矩阵,以得出预测产出。我们用低级别组合矩阵分解来大大加快计算和减少记忆。使用蒙特卡洛方法的数值实验显示了拟议方法的稳健性、效率和可缩缩性。