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Open AccessArticle

Big Data Blind Separation

Department of Systems Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Entropy 2018, 20(3), 150; https://doi.org/10.3390/e20030150
Received: 8 December 2017 / Revised: 23 February 2018 / Accepted: 23 February 2018 / Published: 27 February 2018
(This article belongs to the Special Issue Entropy in Signal Analysis)
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 X R m × N , find A R m × n and S R + n × N such that X = A S . 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. View Full-Text
Keywords: big data; blind signal separation; locally dominant sources; correntropy ranking big data; blind signal separation; locally dominant sources; correntropy ranking
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Syed, M.N. Big Data Blind Separation. Entropy 2018, 20, 150.

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