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

Research output: Contribution to conference without publisher/journal › Poster › Research

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.

Original language	English
Publication date	22 Nov 2018
DOIs	https://doi.org/10.5281/zenodo.1690664
Publication status	Published - 22 Nov 2018
Event	international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques - Centre International de Rencontres Mathématiques, Marseille, France Duration: 21 Nov 2018 → 23 Nov 2018 Conference number: 4 https://sites.google.com/view/itwist18

Workshop

Workshop	international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques
Number	4
Location	Centre International de Rencontres Mathématiques
Country/Territory	France
City	Marseille
Period	21/11/2018 → 23/11/2018
Internet address	https://sites.google.com/view/itwist18

Keywords

compressed sensing
signal processing
estimation theory

Access to Document

10.5281/zenodo.1690664Licence: CC BY 4.0

AUB Link

Search for the material in Aalborg University Library's search engine

1 Finished

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
Project: Research

1 Conference presentations

Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals
Thomas Arildsen (Lecturer)
21 Nov 2018 → 23 Nov 2018
Activity: Talks and presentations › Conference presentations

1 Paper without publisher/journal

Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals
Oxvig, C. S. & Arildsen, T., 22 Nov 2018. 3 p.
Research output: Contribution to conference without publisher/journal › Paper without publisher/journal › Research › peer-review

Open Access
File

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

Cite this

@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

Keywords

Access to Document

AUB Link

Fingerprint

Projects

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

Activities