26 citations · 98 across the 31 of their papers we have counts for
17 papers · 1 filter
Faster Binary Embeddings for Preserving Euclidean Distances
Jinjie Zhang, Rayan Saab
We propose a fast, distance-preserving, binary embedding algorithm to transform a high-dimensional dataset into binary sequences in the cube $\{\…
New Algorithms and Improved Guarantees for One-Bit Compressed Sensing on Manifolds
Mark Iwen, Eric Lybrand, Aaron Nelson +1
We study the problem of approximately recovering signals on a manifold from one-bit linear measurements drawn from either a Gaussian ensemble, partial circulant ensemble, or bounde…
Fast binary embeddings, and quantized compressed sensing with structured matrices
Thang Huynh, Rayan Saab
This paper deals with two related problems, namely distance-preserving binary embeddings and quantization for compressed sensing . First, we propose fast methods to replace points…
Quantization for Low-Rank Matrix Recovery
Eric Lybrand, Rayan Saab
We study Sigma-Delta quantization methods coupled with appropriate reconstruction algorithms for digitizing randomly sampled low-rank matrices. We show that the reconstruction erro…
Quantized Compressed Sensing for Partial Random Circulant Matrices
Joe-Mei Feng, Felix Krahmer, Rayan Saab
We provide the first analysis of a non-trivial quantization scheme for compressed sensing measurements arising from structured measurements. Specifically, our analysis studies comp…
Weighted -Minimization for Sparse Recovery under Arbitrary Prior Information
Deanna Needell, Rayan Saab, Tina Woolf
Weighted -minimization has been studied as a technique for the reconstruction of a sparse signal from compressively sampled measurements when prior information about the si…