Alternating Minimization is a widely used and empirically successful framework for Matrix Completion and related low-rank optimization problems. We give a new algorithm based on Alternating Minimization that provably recovers an unknown low-rank matrix from a random subsample of its entries under a standard incoherence assumption while achieving a linear convergence rate. Compared to previous work our results reduce the provable sample requirements of the Alternating Minimization approach by at least a quartic factor in the rank and the condition number of the unknown matrix. These improvements apply when the matrix is exactly low-rank and when it is only close to low-rank in the Frobenius norm. Underlying our work is a new robust convergence analysis of the well-known Subspace Iteration algorithm for computing the dominant singular vectors of a matrix also known as the Power Method. This viewpoint leads to a conceptually simple understanding of Alternating Minimization that we make use of. Additionally, we contribute a new technique for controlling the coherence of intermediate solutions arising in iterative algorithms. These techniques may be of interest beyond their application here.

使用一种基于交替最小化的新算法，在标准不连贯性假设下，可从一个未知的低秩矩阵中恢复随机子样本的条目，并减少至少一次方之秩和相似矩阵的条件数的交替最小化方法的样本大小要求。

理解矩阵补全的交替最小化方法