NDLI: A class of adaptive algorithms based on ML for non-Gaussian linear filtering

Content Provider	IEEE Xplore Digital Library
Author	Hualiang Li Adali, T.
Copyright Year	2011
Description	Author affiliation: Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 21250, USA (Hualiang Li; Adali, T.)
Abstract	Linear filtering has been extensively studied under the assumption that the noise is Gaussian. The most commonly used least-mean-square-error (LMSE) solution is optimal when the noise is Gaussian. However, in many practical applications, the noise can be modeled more accurately using a non-Gaussian distribution. In this paper, we consider the linear filtering problem where the noise comes from a generalized Gaussian distribution (GGD) and solve the problem using maximum likelihood (ML) estimation. To estimate the likelihood, we consider two approaches. One uses explicitly the GGD form that leads to an adaptive filtering algorithm same as the least mean p-norm (LMP) algorithm [1]. Second approach uses a semi-parametric model where the entropy of the noise is estimated using entropy bound minimization (EBM), a flexible approach to density estimation. We derive the Cramer-Rao lower bound for the ML-LMP estimators by using a second-order Taylor series expansion of the likelihood function. Simulation results show that when the noise comes from a GGD, ML-LMP achieves the best performance, while EBM provides very competitive performance and offers the advantage that it does not assume a certain parametric model for the noise.
Starting Page	1
Ending Page	5
File Size	422990
Page Count	5
File Format	PDF
ISBN	9781424498468
e-ISBN	9781424498482
e-ISBN	9781424498475
DOI	10.1109/CISS.2011.5766110
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	linear mean square error estimation least p-norm algorithm Maximum likelihood estimation Shape Noise Adaptation model Minimization Entropy maximum likelihood estimation
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	Dr. B. Sutradhar bsutra@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	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library 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

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