INDEPENDENT VECTOR ANALYSIS USING SEMI-PARAMETRIC DENSITY ESTIMATION VIA MULTIVARIATE ENTROPY MAXIMIZATION

Simple item page
dc.contributor.authorDamasceno, Lucas P.
dc.contributor.authorCavalcante, Charles C.
dc.contributor.authorAdali, Tulay
dc.contributor.authorBoukouvalas, Zois
dc.date.accessioned2022-06-28T20:36:52Z
dc.date.available2022-06-28T20:36:52Z
dc.date.issued2021-06-17
dc.description.abstractDue to the wide use of multi-sensor technology, analysis of multiple sets of data is at the heart of many challenging engineering problems. Independent vector analysis (IVA), a recent generalization of independent component analysis (ICA), enables the joint analysis of datasets and extraction of latent sources through the use of a simple yet effective generative model. However, the success of IVA is tied to proper estimation of the probability density function (PDF) of the multivariate latent sources; information that is generally unknown. In this work, we propose a new flexible and efficient multivariate PDF estimation technique based on the maximum entropy principle and apply this technique to the development of an effective IVA algorithm that successfully matches multivariate latent sources from a wide range of distributions. We verify the effectiveness of the new estimation technique and further demonstrate the superior performance of the new IVA algorithm numerically using simulated data.en_US
dc.description.urihttps://sigport.org/documents/independent-vector-analysis-using-semi-parametric-density-estimation-multivariate-entropyen_US
dc.genrepresentations (communicative events)en_US
dc.identifierdoi:10.13016/m2sjo5-oabw
dc.identifier.urihttp://hdl.handle.net/11603/25076
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.subjectjoint blind source separationen_US
dc.subjectIVA algorithmen_US
dc.subjectmultivariate probability density estimatoren_US
dc.titleINDEPENDENT VECTOR ANALYSIS USING SEMI-PARAMETRIC DENSITY ESTIMATION VIA MULTIVARIATE ENTROPY MAXIMIZATIONen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0003-0594-2796en_US

Files

License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection