Recovery of Sparse and Low Rank Components of Matrices Using Iterative Method with Adaptive Thresholding
This work addresses matrix decomposition for applications with non-sparse noise, but it appears incremental as it builds on existing fast methods.
The authors tackled the problem of recovering sparse and low-rank components from matrices by proposing an iterative algorithm with adaptive thresholding, which demonstrated suitable performance and low run-time in simulations.
In this letter, we propose an algorithm for recovery of sparse and low rank components of matrices using an iterative method with adaptive thresholding. In each iteration, the low rank and sparse components are obtained using a thresholding operator. This algorithm is fast and can be implemented easily. We compare it with one of the most common fast methods in which the rank and sparsity are approximated by $\ell_1$ norm. We also apply it to some real applications where the noise is not so sparse. The simulation results show that it has a suitable performance with low run-time.