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

Christian Schou Oxvig, Thomas Arildsen

Publikation: Konferencebidrag uden forlag/tidsskriftPaper uden forlag/tidsskriftForskningpeer review

47 Downloads (Pure)

Resumé

Generalised approximate message passing (GAMP) is an approximate Bayesian estimation algorithm for signals observed through a linear transform with a possibly non-linear subsequent measurement model. By leveraging prior information about the observed signal, such as sparsity in a known dictionary, GAMP can for example reconstruct 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. Here we present sparse signal priors for GAMP to estimate non-i.d.d. signals through a non-uniform weighting of the input prior, for example allowing GAMP to support model-based compressed sensing.
OriginalsprogEngelsk
Publikationsdato22 nov. 2018
Antal sider3
StatusUdgivet - 22 nov. 2018
Begivenhedinternational Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques - Centre International de Rencontres Mathématiques, Marseille, Frankrig
Varighed: 21 nov. 201823 nov. 2018
Konferencens nummer: 4
https://sites.google.com/view/itwist18

Workshop

Workshopinternational Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques
Nummer4
LokationCentre International de Rencontres Mathématiques
LandFrankrig
ByMarseille
Periode21/11/201823/11/2018
Internetadresse

Fingerprint

Message passing
Compressed sensing
Glossaries

Citer dette

Oxvig, C. S., & Arildsen, T. (2018). Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals. Afhandling præsenteret på international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Marseille, Frankrig.
Oxvig, Christian Schou ; Arildsen, Thomas. / Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals. Afhandling præsenteret på international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Marseille, Frankrig.3 s.
@conference{3c927b25be864633b81c177ae66e9b95,
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 subsequent measurement model. By leveraging prior information about the observed signal, such as sparsity in a known dictionary, GAMP can for example reconstruct 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. Here we present sparse signal priors for GAMP to estimate non-i.d.d. signals through a non-uniform weighting of the input prior, for example allowing GAMP to support model-based compressed sensing.",
keywords = "compressed sensing, signal processing, estimation theory",
author = "Oxvig, {Christian Schou} and Thomas Arildsen",
year = "2018",
month = "11",
day = "22",
language = "English",
note = "null ; Conference date: 21-11-2018 Through 23-11-2018",
url = "https://sites.google.com/view/itwist18",

}

Oxvig, CS & Arildsen, T 2018, 'Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals' Paper fremlagt ved international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Marseille, Frankrig, 21/11/2018 - 23/11/2018, .

Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals. / Oxvig, Christian Schou; Arildsen, Thomas.

2018. Afhandling præsenteret på international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Marseille, Frankrig.

Publikation: Konferencebidrag uden forlag/tidsskriftPaper uden forlag/tidsskriftForskningpeer review

TY - CONF

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

AU - Oxvig, Christian Schou

AU - Arildsen, Thomas

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 subsequent measurement model. By leveraging prior information about the observed signal, such as sparsity in a known dictionary, GAMP can for example reconstruct 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. Here we present sparse signal priors for GAMP to estimate non-i.d.d. signals through a non-uniform weighting of the input prior, for example allowing GAMP to support model-based compressed sensing.

AB - Generalised approximate message passing (GAMP) is an approximate Bayesian estimation algorithm for signals observed through a linear transform with a possibly non-linear subsequent measurement model. By leveraging prior information about the observed signal, such as sparsity in a known dictionary, GAMP can for example reconstruct 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. Here we present sparse signal priors for GAMP to estimate non-i.d.d. signals through a non-uniform weighting of the input prior, for example allowing GAMP to support model-based compressed sensing.

KW - compressed sensing

KW - signal processing

KW - estimation theory

UR - https://doi.org/10.5281/zenodo.1409655

UR - https://docs.google.com/spreadsheets/d/e/2PACX-1vSWjLALqTnovorQQmhKyGAoPOXHaESJWMowskOYz1vNdo1ZbgW0jV6giu9yVxpduAAB3Bl0HE-QbYq_/pubhtml?gid=1304672187&single=true&widget=false&headers=false&chrome=false

M3 - Paper without publisher/journal

ER -

Oxvig CS, Arildsen T. Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals. 2018. Afhandling præsenteret på international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Marseille, Frankrig.