Skip to main navigation Skip to search Skip to main content

2/ℓ2-foreach sparse recovery with low risk

  • Anna C. Gilbert
  • , Hung Q. Ngo
  • , Ely Porat
  • , Atri Rudra
  • , Martin J. Strauss
  • University of Michigan, Ann Arbor
  • SUNY Buffalo
  • Bar-Ilan University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

30 Scopus citations

Abstract

In this paper, we consider the "foreach" sparse recovery problem with failure probability p. The goal of the problem is to design a distribution over m x N matrices Φ and a decoding algorithm A such that for every x ∈ ℝN, we have with probability at least 1 - p ∥x - A(Φx∥2 ≤ C∥x - xk2, where xk is the best k-sparse approximation of x. Our two main results are: (1) We prove a lower bound on m, the number measurements, of Ω(k log(n/k) + log(1/p)) for 2-Θ(N) ≤ p < 1. Cohen, Dahmen, and DeVore [4] prove that this bound is tight. (2) We prove nearly matching upper bounds that also admit sub-linear time decoding. Previous such results were obtained only when p = Ω(1). One corollary of our result is an an extension of Gilbert et al. [6] results for information-theoretically bounded adversaries.

Original languageEnglish
Title of host publicationAutomata, Languages, and Programming - 40th International Colloquium, ICALP 2013, Proceedings
Pages461-472
Number of pages12
EditionPART 1
DOIs
StatePublished - 2013
Event40th International Colloquium on Automata, Languages, and Programming, ICALP 2013 - Riga, Latvia
Duration: Jul 8 2013Jul 12 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7965 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference40th International Colloquium on Automata, Languages, and Programming, ICALP 2013
Country/TerritoryLatvia
CityRiga
Period07/8/1307/12/13

Fingerprint

Dive into the research topics of 'ℓ2/ℓ2-foreach sparse recovery with low risk'. Together they form a unique fingerprint.

Cite this