论文标题
混合DATA矩阵完成的广义潜在因子模型方法具有入门式一致性
A Generalized Latent Factor Model Approach to Mixed-data Matrix Completion with Entrywise Consistency
论文作者
论文摘要
矩阵完成是一类机器学习方法，涉及部分观察到的矩阵中缺失条目的预测。本文研究了混合数据的矩阵完成，即涉及变量混合类型的数据（例如，连续，二元，序数）。我们将其作为一般的非线性因子模型家族的低级矩阵估计问题提出，然后提出进入一致的估计器来估计低级数矩阵。为提出的估计器得出了紧密的概率误差界限。提出的方法通过模拟研究和实际数据应用程序进行协作过滤和大规模教育评估评估。
Matrix completion is a class of machine learning methods that concerns the prediction of missing entries in a partially observed matrix. This paper studies matrix completion for mixed data, i.e., data involving mixed types of variables (e.g., continuous, binary, ordinal). We formulate it as a low-rank matrix estimation problem under a general family of non-linear factor models and then propose entrywise consistent estimators for estimating the low-rank matrix. Tight probabilistic error bounds are derived for the proposed estimators. The proposed methods are evaluated by simulation studies and real-data applications for collaborative filtering and large-scale educational assessment.
