umu.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
On q-ratio CMSV for sparse recovery
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik. (Mathematical Statistics)ORCID-id: 0000-0001-5673-620x
2019 (engelsk)Inngår i: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 165, s. 128-132Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

As a kind of computable incoherence measure of the measurement matrix, q-ratio constrained minimal singular values (CMSV) was proposed in Zhou and Yu (2019) to derive the performance bounds for sparse recovery. In this paper, we study the geometrical properties of the q-ratio CMSV, based on which we establish new sufficient conditions for signal recovery involving both sparsity defect and measurement error. The ℓ1-truncated set q-width of the measurement matrix is developed as the geometrical characterization of q-ratio CMSV. In addition, we show that the q-ratio CMSVs of a class of structured random matrices are bounded away from zero with high probability as long as the number of measurements is large enough, therefore these structured random matrices satisfy those established sufficient conditions. Overall, our results generalize the results in Zhang and Cheng (2012) from q=2 to any q ∈ (1, ∞] and complement the arguments of q-ratio CMSV from a geometrical view.

sted, utgiver, år, opplag, sider
Elsevier, 2019. Vol. 165, s. 128-132
Emneord [en]
Sparse recovery, q-ratio sparsity, q-ratio constrained minimal singular values, ℓ1-truncated set q-width
HSV kategori
Forskningsprogram
matematisk statistik
Identifikatorer
URN: urn:nbn:se:umu:diva-161379DOI: 10.1016/j.sigpro.2019.07.003ISI: 000485855000012OAI: oai:DiVA.org:umu-161379DiVA, id: diva2:1334627
Ingår i projekt
Statistiska modeller och intelligenta datainsamlingsmetoder för MRI och PET mätningar med tillämpning för monitoring av cancerbehandling, Swedish Research Council
Forskningsfinansiär
Swedish Research Council, 340-2013-5342Tilgjengelig fra: 2019-07-03 Laget: 2019-07-03 Sist oppdatert: 2019-11-11bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Personposter BETA

Zhou, ZhiyongYu, Jun

Søk i DiVA

Av forfatter/redaktør
Zhou, ZhiyongYu, Jun
Av organisasjonen
I samme tidsskrift
Signal Processing

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 65 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf