Alternating Least-Squares for Low-Rank Matrix Reconstruction
Alternating Least-Squares for Low-Rank Matrix Reconstruction
For reconstruction of low-rank matrices from undersampled measurements, we develop an iterative algorithm based on least-squares estimation. While the algorithm can be used for any low-rank matrix, it is also capable of exploiting a-priori knowledge of matrix structure. In particular, we consider linearly structured matrices, such as Hankel and Toeplitz, …