Robust principal component analysis (RPCA) is a well-studied problem whose goal is to
decompose a matrix into the sum of low-rank and sparse components. In this paper, we …

Large-scale linear systems,, frequently arise in practice and demand effective iterative
solvers. Often, these systems are noisy due to operational errors or faulty data-collection …

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 …

In tracking of time-varying low-rank models of time-varying matrices, we present a method
robust to both uniformly-distributed measurement noise and arbitrarily-distributed “sparse” …

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 …

Large-scale linear systems, $ Ax= b $, frequently arise in practice and demand effective
iterative solvers. Often, these systems are noisy due to operational errors or faulty data …

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 …

Given two disjoint sets, the best pair problem aims to find a point in one set and another
point in the other set with minimal distance between them. In this paper, we formulate the …

Matrix low rank approximation including the classical PCA and the robust PCA (RPCA)
method have been applied to solve the background modeling problem in video analysis …