Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …

Recently, a variety of super-resolution (SR) methods have been devoted to enhancing the
angular resolution of real beam mapping (RBM) imagery in modern microwave remote …

SUMMARY Gravity Recovery and Climate Experiment (GRACE) Spherical Harmonics (SH)
solutions are usually provided together with the corresponding formal errors, however, all …

We study a weighted low-rank approximation that is inspired by a problem of constrained
low-rank approximation of matrices as initiated by the work of Golub, Hoffman, and Stewart …

We primarily study a special a weighted low-rank approximation of matrices and then apply
it to solve the background modeling problem. We propose two algorithms for this purpose …

Classical principal component analysis (PCA) is not robust when the data contain sparse
outliers. The use of the l_1 norm in the Robust PCA (RPCA) method successfully eliminates …

Principal component pursuit (PCP) is a state-of-the-art approach to background estimation
problems. Due to their higher computational cost, PCP algorithms, such as robust principal …

We propose a surprisingly simple model to estimate supervised video backgrounds. Our
model is based on L1 regression. As existing methods for L1 regression do not scale to high …

This paper is devoted to proposing a general weighted low-rank recovery model and
designing a fast SVD-free computational scheme to solve it. First, our generic weighted low …

Sequential consistency is a well known consistency requirement for distributed shared
memory. However, most of the algorithms that implement sequential consistency involve …