论文标题
自适应嘈杂矩阵完成
Adaptive Noisy Matrix Completion
论文作者
论文摘要
在各种类型的类别下，已经对低级矩阵的完成进行了广泛的研究。该问题可以归类为嘈杂的完成或确切的完成，也可以将活跃或被动完成算法分类。在本文中，我们专注于具有有界噪声类型的自适应矩阵完成。我们假设矩阵$ \ mathbf {m} $我们要恢复的目标是添加有界小噪声的低率矩阵。 \ cite {nina}以前已经在固定采样模型中研究了该问题。在这里，我们在自适应环境中研究了这个问题，我们不断估计与基础低率子空间和噪音添加的子空间的角度的上限。此外，这里建议的方法可以显示比上述方法要小得多。
Low-rank matrix completion has been studied extensively under various type of categories. The problem could be categorized as noisy completion or exact completion, also active or passive completion algorithms. In this paper we focus on adaptive matrix completion with bounded type of noise. We assume that the matrix $\mathbf{M}$ we target to recover is composed as low-rank matrix with addition of bounded small noise. The problem has been previously studied by \cite{nina}, in a fixed sampling model. Here, we study this problem in adaptive setting that, we continuously estimate an upper bound for the angle with the underlying low-rank subspace and noise-added subspace. Moreover, the method suggested here, could be shown requires much smaller observation than aforementioned method.
