3 citations · 12 across the 27 of their papers we have counts for
8 papers · 1 filter
On audio enhancement via online non-negative matrix factorization
Andrew Sack, Wenzhao Jiang, Michael Perlmutter +2
We propose a method for noise reduction, the task of producing a clean audio signal from a recording corrupted by additive noise. Many common approaches to this problem are based u…
Robust recovery of bandlimited graph signals via randomized dynamical sampling
Longxiu Huang, Deanna Needell, Sui Tang
Heat diffusion processes have found wide applications in modelling dynamical systems over graphs. In this paper, we consider the recovery of a -bandlimited graph signal that is…
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…
Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition
HanQin Cai, Zehan Chao, Longxiu Huang +1
We study the problem of tensor robust principal component analysis (TRPCA), which aims to separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their…
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…
Statistical Learning for Best Practices in Tattoo Removal
Richard Yim, Jamie Haddock, Deanna Needell
The causes behind complications in laser-assisted tattoo removal are currently not well understood, and in the literature relating to tattoo removal the emphasis on removal treatme…