3 citations · 10 across the 20 of their papers we have counts for
10 papers · 1 filter
Modewise Operators, the Tensor Restricted Isometry Property, and Low-Rank Tensor Recovery
Mark A. Iwen, Deanna Needell, Michael Perlmutter +1
Recovery of sparse vectors and low-rank matrices from a small number of linear measurements is well-known to be possible under various model assumptions on the measurements. The ke…
QuantileRK: Solving Large-Scale Linear Systems with Corrupted, Noisy Data
Benjamin Jarman, Deanna Needell
Measurement data in linear systems arising from real-world applications often suffers from both large, sparse corruptions, and widespread small-scale noise. This can render many po…
Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions
HanQin Cai, Keaton Hamm, Longxiu Huang +1
Low rank tensor approximation is a fundamental tool in modern machine learning and data science. In this paper, we study the characterization, perturbation analysis, and an efficie…
Tensor Completion through Total Variationwith Initialization from Weighted HOSVD
Zehan Chao, Longxiu Huang, Deanna Needell
In our paper, we have studied the tensor completion problem when the sampling pattern is deterministic. We first propose a simple but efficient weighted HOSVD algorithm for recover…
Randomized Kaczmarz with Averaging
Jacob D. Moorman, Thomas K. Tu, Denali Molitor +1
The randomized Kaczmarz (RK) method is an iterative method for approximating the least-squares solution of large linear systems of equations. The standard RK method uses sequential…
Sketching for Motzkin's Iterative Method for Linear Systems
Elizaveta Rebrova, Deanna Needell
Projection-based iterative methods for solving large over-determined linear systems are well-known for their simplicity and computational efficiency. It is also known that the corr…