Sparsity and Independence: Balancing Two Objectives in Optimization for Source Separation with Application to fMRI Analysis

dc.contributor.authorBoukouvalas, Zois
dc.contributor.authorLevin-Schwartz, Yuri
dc.contributor.authorCalhoun, Vince D.
dc.contributor.authorAdali, Tulay
dc.date.accessioned2020-07-29T18:40:25Z
dc.date.available2020-07-29T18:40:25Z
dc.date.issued2017-06-11
dc.description.abstractBecause of its wide applicability in various disciplines, blind source separation (BSS), has been an active area of research. For a given dataset, BSS provides useful decompositions under minimum assumptions typically by making use of statistical properties—types of diversity—of the data. Two popular types of diversity that have proven useful for many applications are statistical independence and sparsity. Although many methods have been proposed for the solution of the BSS problem that take either the statistical independence or the sparsity of the data into account, there is no unified method that can take into account both types of diversity simultaneously. In this work, we provide a mathematical framework that enables direct control over the influence of these two types of diversity and apply the proposed framework to the development of an effective ICA algorithm that can jointly exploit independence and sparsity. In addition, due to its importance in biomedical applications, we propose a new model reproducibility framework for the evaluation of the proposed algorithm. Using simulated functional magnetic resonance imaging (fMRI) data, we study the trade-offs between the use of sparsity versus independence in terms of the separation accuracy and reproducibility of the algorithm and provide guidance on how to balance these two objectives in real world applications where the ground truth is not available.en_US
dc.description.sponsorshipThis work was supported in part by the NSF grants 1631838, 1539067, and 1618551, and the NIH-NIBIB grant R01EB020407en_US
dc.description.urihttps://www.sciencedirect.com/science/article/abs/pii/S0016003217303174en_US
dc.format.extent18 pagesen_US
dc.genrejournal articles preprintsen_US
dc.identifierdoi:10.13016/m2ljxi-jwp2
dc.identifier.citationZois Boukouvalas, Yuri Levin-Schwartz, Vince D. Calhoun, Tulay Adalı, Sparsity and Independence: Balancing Two Objectives in Optimization for Source Separation with Application to fMRI Analysis, Journal of the Franklin Institute Volume 355, Issue 4, Pages 1873-1887 (2018), https://doi.org/10.1016/j.jfranklin.2017.07.003en_US
dc.identifier.urihttps://doi.org/10.1016/j.jfranklin.2017.07.003
dc.identifier.urihttp://hdl.handle.net/11603/19279
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
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.
dc.rights© 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
dc.subjectUMBC High Performance Computing Facility (HPCF)
dc.titleSparsity and Independence: Balancing Two Objectives in Optimization for Source Separation with Application to fMRI Analysisen_US
dc.typeTexten_US

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