Going Off the Grid: Iterative Model Selection for Biclustered Matrix Completion
研究利用行和列相似性信息进行矩阵补全，提出一种迭代方法直接最小化信息准则来选择平滑程度，并通过Hutchinson估计器和随机拟牛顿法提高计算效率。
We consider the problem of performing matrix completion with side information on row-by-row and column-by-column similarities. We build upon recent proposals for matrix estimation with smoothness constraints with respect to row and column graphs. We present a novel iterative procedure for directly minimizing an information criterion to select an appropriate amount of row and column smoothing, namely, to perform model selection. We also discuss how to exploit the special structure of the problem to scale up the estimation and model selection procedure via the Hutchinson estimator, combined with a stochastic Quasi-Newton approach. Supplementary material for this article is available online.