NDLI: An empirical-bayes approach to recovering linearly constrained non-negative sparse signals

Content Provider	IEEE Xplore Digital Library
Author	Vila, J. Schniter, P.
Copyright Year	2013
Description	Author affiliation: Dept. of ECE, Ohio State Univ., Columbus, OH, USA (Vila, J.; Schniter, P.)
Abstract	We consider the recovery of an (approximately) sparse signal from noisy linear measurements, in the case that the signal is apriori known to be non-negative and obeys certain linear equality constraints. For this, we propose a novel empirical-Bayes approach that combines the Generalized Approximate Message Passing (GAMP) algorithm with the expectation maximization (EM) algorithm. To enforce both sparsity and non-negativity, we employ an i.i.d Bernoulli non-negative Gaussian mixture (NNGM) prior and perform approximate minimum mean-squared error (MMSE) recovery of the signal using sum-product GAMP. To learn the NNGM parameters, we use the EM algorithm with a suitable initialization. Meanwhile, the linear equality constraints are enforced by augmenting GAMP's linear observation model with noiseless pseudo-measurements. Numerical experiments demonstrate the state-of-the art mean-squared-error and runtime of our approach.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	5
Ending Page	8
File Size	413215
Page Count	4
File Format	PDF
e-ISBN	9781467331463
DOI	10.1109/CAMSAP.2013.6713993
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-15
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Runtime Message passing Conferences Approximation algorithms Noise measurement Approximation methods Standards
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

1 An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals

An empirical-Bayes approach to recovering linearly constrained non-negative sparse signals (2013)

An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals

An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals

An empirical-bayes approach to recovering linearly constrained non-negative sparse signals.

Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals

Sparse Bayesian learning using approximate message passing

Optimal recovery from compressive measurements via denoising-based approximate message passing

Approximate message passing under finite alphabet constraints

An empirical-bayes approach to recovering linearly constrained non-negative sparse signals

Similar Documents

1 An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals

An empirical-Bayes approach to recovering linearly constrained non-negative sparse signals (2013)

An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals

An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals

An empirical-bayes approach to recovering linearly constrained non-negative sparse signals.

Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals

Sparse Bayesian learning using approximate message passing

Optimal recovery from compressive measurements via denoising-based approximate message passing

Approximate message passing under finite alphabet constraints

An empirical-bayes approach to recovering linearly constrained non-negative sparse signals