7 citations · 18 across the 8 of their papers we have counts for
9 papers
A Note on Randomized Kaczmarz Algorithm for Solving Doubly-Noisy Linear Systems
El Houcine Bergou, Soumia Boucherouite, Aritra Dutta +2
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-col…
Low-Rank Phase Retrieval with Structured Tensor Models
Soo Min Kwon, Xin Li, Anand D. Sarwate
We study the low-rank phase retrieval problem, where the objective is to recover a sequence of signals (typically images) given the magnitude of linear measurements of those signal…
An Adaptive Rank Continuation Algorithm for General Weighted Low-rank Recovery
Aritra Dutta, Jingwei Liang, Xin Li
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-rank r…
Weighted Low-Rank Approximation of Matrices and Background Modeling
Aritra Dutta, Xin Li, Peter Richtarik
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: o…
Weighted Low Rank Approximation for Background Estimation Problems
Aritra Dutta, Xin Li
Classical principal component analysis (PCA) is not robust to the presence of sparse outliers in the data. The use of the norm in the Robust PCA (RPCA) method successfully…
A Batch-Incremental Video Background Estimation Model using Weighted Low-Rank Approximation of Matrices
Aritra Dutta, Xin Li, Peter Richtárik
Principal component pursuit (PCP) is a state-of-the-art approach for background estimation problems. Due to their higher computational cost, PCP algorithms, such as robust principa…