323 citations · 1.3k across the 121 of their papers we have counts for
7 papers · 1 filter
Methods for Quantized Compressed Sensing
Hao-Jun Michael Shi, Mindy Case, Xiaoyi Gu +2
In this paper, we compare and catalog the performance of various greedy quantized compressed sensing algorithms that reconstruct sparse signals from quantized compressed measuremen…
A note on practical approximate projection schemes in signal space methods
Xiaoyi Gu, Deanna Needell, Shenyinying Tu
Compressive sensing (CS) is a new technology which allows the acquisition of signals directly in compressed form, using far fewer measurements than traditional theory dictates. Rec…
Rows vs. Columns: Randomized Kaczmarz or Gauss-Seidel for Ridge Regression
Ahmed Hefny, Deanna Needell, Aaditya Ramdas
The Kaczmarz and Gauss-Seidel methods aim to solve a linear system by iteratively refining the solution estimate; the fo…
One-Bit Compressive Sensing with Partial Support
Phillip North, Deanna Needell
The Compressive Sensing framework maintains relevance even when the available measurements are subject to extreme quantization, as is exemplified by the so-called one-bit compresse…
Constrained adaptive sensing
Mark A. Davenport, Andrew K. Massimino, Deanna Needell +1
Suppose that we wish to estimate a vector from a small number of noisy linear measurements of the form , where…
Convergence properties of the randomized extended Gauss-Seidel and Kaczmarz methods
Anna Ma, Deanna Needell, Aaditya Ramdas
The Kaczmarz and Gauss-Seidel methods both solve a linear system by iteratively refining the solution estimate. Recent interest in these methods has been spar…