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Big Data Blind Separation

Data or signal separation is one of the critical areas of data analysis. In this work, the problem of non-negative data separation is considered. The problem can be briefly described as follows: given [Formula: see text] , find [Formula: see text] and [Formula: see text] such that [Formula: see text...

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Autor principal: Syed, Mujahid N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512668/
https://www.ncbi.nlm.nih.gov/pubmed/33265241
http://dx.doi.org/10.3390/e20030150
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author Syed, Mujahid N.
author_facet Syed, Mujahid N.
author_sort Syed, Mujahid N.
collection PubMed
description Data or signal separation is one of the critical areas of data analysis. In this work, the problem of non-negative data separation is considered. The problem can be briefly described as follows: given [Formula: see text] , find [Formula: see text] and [Formula: see text] such that [Formula: see text]. Specifically, the problem with sparse locally dominant sources is addressed in this work. Although the problem is well studied in the literature, a test to validate the locally dominant assumption is not yet available. In addition to that, the typical approaches available in the literature sequentially extract the elements of the mixing matrix. In this work, a mathematical modeling-based approach is presented that can simultaneously validate the assumption, and separate the given mixture data. In addition to that, a correntropy-based measure is proposed to reduce the model size. The approach presented in this paper is suitable for big data separation. Numerical experiments are conducted to illustrate the performance and validity of the proposed approach.
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spelling pubmed-75126682020-11-09 Big Data Blind Separation Syed, Mujahid N. Entropy (Basel) Article Data or signal separation is one of the critical areas of data analysis. In this work, the problem of non-negative data separation is considered. The problem can be briefly described as follows: given [Formula: see text] , find [Formula: see text] and [Formula: see text] such that [Formula: see text]. Specifically, the problem with sparse locally dominant sources is addressed in this work. Although the problem is well studied in the literature, a test to validate the locally dominant assumption is not yet available. In addition to that, the typical approaches available in the literature sequentially extract the elements of the mixing matrix. In this work, a mathematical modeling-based approach is presented that can simultaneously validate the assumption, and separate the given mixture data. In addition to that, a correntropy-based measure is proposed to reduce the model size. The approach presented in this paper is suitable for big data separation. Numerical experiments are conducted to illustrate the performance and validity of the proposed approach. MDPI 2018-02-27 /pmc/articles/PMC7512668/ /pubmed/33265241 http://dx.doi.org/10.3390/e20030150 Text en © 2018 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Syed, Mujahid N.
Big Data Blind Separation
title Big Data Blind Separation
title_full Big Data Blind Separation
title_fullStr Big Data Blind Separation
title_full_unstemmed Big Data Blind Separation
title_short Big Data Blind Separation
title_sort big data blind separation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512668/
https://www.ncbi.nlm.nih.gov/pubmed/33265241
http://dx.doi.org/10.3390/e20030150
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