Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals

Christian Schou Oxvig; Thomas Arildsen

doi:10.5281/zenodo.1690664

Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals

Christian Schou Oxvig, Thomas Arildsen

Publikation: Konferencebidrag uden forlag/tidsskrift › Poster › Forskning

Abstract

Generalised approximate message passing (GAMP) is an approximate Bayesian estimation algorithm for signals observed through a linear transform with a possibly non-linear measurement model.
By leveraging prior information about the observed signal, such as sparsity in a known dictionary, GAMP enables reconstructing signals from under-determined measurements – known as compressed sensing.
In the sparse signal setting, most existing signal priors for GAMP assume the input signal to have i.i.d. entries.
We present sparse signal priors to estimate non-identically distributed signals through a non-uniform weighting, e.g. enabling model-based compressed sensing with GAMP.

Originalsprog	Engelsk
Publikationsdato	22 nov. 2018
DOI	https://doi.org/10.5281/zenodo.1690664
Status	Udgivet - 22 nov. 2018
Begivenhed	international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques - Centre International de Rencontres Mathématiques, Marseille, Frankrig Varighed: 21 nov. 2018 → 23 nov. 2018 Konferencens nummer: 4 https://sites.google.com/view/itwist18

Workshop

Workshop	international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques
Nummer	4
Lokation	Centre International de Rencontres Mathématiques
Land/Område	Frankrig
By	Marseille
Periode	21/11/2018 → 23/11/2018
Internetadresse	https://sites.google.com/view/itwist18

Adgang til dokumentet

10.5281/zenodo.1690664Licens: CC BY 4.0

AUB Link

Søg efter materialet i Aalborg Universitetsbiblioteks søgemaskine

1 Afsluttet

FastAFM: Enabling Fast Image Acquisition for Atomic Force Microscopy using Compressed Sensing
Larsen, T., Østergaard, J., Jensen, T., Arildsen, T., Oxvig, C. S. & Pedersen, P. S.
01/09/2013 → 31/08/2016
Projekter: Projekt › Forskning

1 Konferenceoplæg

Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals
Thomas Arildsen (Foredragsholder)
21 nov. 2018 → 23 nov. 2018
Aktivitet: Foredrag og mundtlige bidrag › Konferenceoplæg

1 Paper uden forlag/tidsskrift

Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals
Oxvig, C. S. & Arildsen, T., 22 nov. 2018. 3 s.
Publikation: Konferencebidrag uden forlag/tidsskrift › Paper uden forlag/tidsskrift › Forskning › peer review

Åben adgang
Fil

Weighted GAMP Phase Transitions
Oxvig, C. S. (Ophavsperson), Arildsen, T. (Ophavsperson) & Larsen, T. (Ophavsperson), Zenodo, 5 sep. 2018
DOI: 10.5281/zenodo.1409655
Datasæt

Citationsformater

@conference{a40661c02aa4454bbe8bffcd52142827,

title = "Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals",

abstract = "Generalised approximate message passing (GAMP) is an approximate Bayesian estimation algorithm for signals observed through a linear transform with a possibly non-linear measurement model.By leveraging prior information about the observed signal, such as sparsity in a known dictionary, GAMP enables reconstructing signals from under-determined measurements – known as compressed sensing.In the sparse signal setting, most existing signal priors for GAMP assume the input signal to have i.i.d. entries.We present sparse signal priors to estimate non-identically distributed signals through a non-uniform weighting, e.g. enabling model-based compressed sensing with GAMP.",

keywords = "compressed sensing, signal processing, estimation theory",

author = "Oxvig, {Christian Schou} and Thomas Arildsen",

year = "2018",

month = nov,

day = "22",

doi = "10.5281/zenodo.1690664",

language = "English",

note = "international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques ; Conference date: 21-11-2018 Through 23-11-2018",

url = "https://sites.google.com/view/itwist18",

}

TY - CONF

T1 - Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals

AU - Oxvig, Christian Schou

AU - Arildsen, Thomas

N1 - Conference code: 4

PY - 2018/11/22

Y1 - 2018/11/22

N2 - Generalised approximate message passing (GAMP) is an approximate Bayesian estimation algorithm for signals observed through a linear transform with a possibly non-linear measurement model.By leveraging prior information about the observed signal, such as sparsity in a known dictionary, GAMP enables reconstructing signals from under-determined measurements – known as compressed sensing.In the sparse signal setting, most existing signal priors for GAMP assume the input signal to have i.i.d. entries.We present sparse signal priors to estimate non-identically distributed signals through a non-uniform weighting, e.g. enabling model-based compressed sensing with GAMP.

AB - Generalised approximate message passing (GAMP) is an approximate Bayesian estimation algorithm for signals observed through a linear transform with a possibly non-linear measurement model.By leveraging prior information about the observed signal, such as sparsity in a known dictionary, GAMP enables reconstructing signals from under-determined measurements – known as compressed sensing.In the sparse signal setting, most existing signal priors for GAMP assume the input signal to have i.i.d. entries.We present sparse signal priors to estimate non-identically distributed signals through a non-uniform weighting, e.g. enabling model-based compressed sensing with GAMP.

KW - compressed sensing

KW - signal processing

KW - estimation theory

U2 - 10.5281/zenodo.1690664

DO - 10.5281/zenodo.1690664

M3 - Poster

T2 - international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques

Y2 - 21 November 2018 through 23 November 2018

ER -

Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals

Abstract

Workshop

Adgang til dokumentet

AUB Link

Fingeraftryk

Projekter

FastAFM: Enabling Fast Image Acquisition for Atomic Force Microscopy using Compressed Sensing

Aktiviteter