323 citations · 1.3k across the 121 of their papers we have counts for
9 papers · 1 filter
Practical approximate projection schemes in greedy signal space methods
Chris Garnatz, Xiaoyi Gu, Alison Kingman +3
Compressive sensing (CS) is a new signal acquisition paradigm which shows that far fewer samples are required to reconstruct sparse signals than previously thought. Although most o…
Exponential decay of reconstruction error from binary measurements of sparse signals
Richard Baraniuk, Simon Foucart, Deanna Needell +2
Binary measurements arise naturally in a variety of statistical and engineering applications. They may be inherent to the problem---e.g., in determining the relationship between ge…
Linear Convergence of Stochastic Iterative Greedy Algorithms with Sparse Constraints
Nam Nguyen, Deanna Needell, Tina Woolf
Motivated by recent work on stochastic gradient descent methods, we develop two stochastic variants of greedy algorithms for possibly non-convex optimization problems with sparsity…
Block Kaczmarz Method with Inequalities
Jonathan Briskman, Deanna Needell
The randomized Kaczmarz method is an iterative algorithm that solves overdetermined systems of linear equations. Recently, the method was extended to systems of equalities and ineq…
Two-Part Reconstruction with Noisy-Sudocodes
Yanting Ma, Dror Baron, Deanna Needell
We develop a two-part reconstruction framework for signal recovery in compressed sensing (CS), where a fast algorithm is applied to provide partial recovery in Part 1, and a CS alg…
Improving Image Clustering using Sparse Text and the Wisdom of the Crowds
Anna Ma, Arjuna Flenner, Deanna Needell +1
We propose a method to improve image clustering using sparse text and the wisdom of the crowds. In particular, we present a method to fuse two different kinds of document features,…