This paper studies the Schatten-$q$ error of low-rank matrix estimation by singular value decomposition under perturbation. Specifically, we establish a tight perturbation bound on the low-rank matrix estimation via a perturbation projection error bound. This new proof technique has provable advantages over the classic approaches. Then, we establish lower bounds to justify the tightness of the upper bound on the low-rank matrix estimation error. Based on the matrix perturbation projection error bound, we further develop a unilateral and a user-friendly sin$\Theta$ bound for singular subspace perturbation. Finally, we demonstrate the advantage of our results over the ones in the literature by simulation.
翻译:本文研究了低级矩阵估计的Schaten-$q$差错,以单值在扰动下分解。 具体地说, 我们通过扰动预测误差对低级矩阵估计进行严格扰动。 这一新的证据技术比经典方法更具有可辨别的优势。 然后, 我们建立较低的界限, 以证明低级矩阵估计误差上限的紧性。 根据矩阵扰动预测误差, 我们进一步开发了单方和方便用户的sin$\Theta$, 用于单子空间渗透。 最后, 我们通过模拟在文献中展示了我们的结果优于这些结果的优势 。