An efficient analytic solution for joint blind source separation

Simple item page
dc.contributor.authorGabrielson, Ben
dc.contributor.authorAkhonda, M. A. B. S.
dc.contributor.authorLehmann, Isabell
dc.contributor.authorAdali, Tulay
dc.date.accessioned2024-05-29T14:38:19Z
dc.date.available2024-05-29T14:38:19Z
dc.date.issued2024-05-07
dc.description.abstractJoint blind source separation (JBSS) is a powerful methodology for analyzing multiple related datasets, able to jointly extract sources that describe statistical dependencies across the datasets. However, JBSS can be computationally prohibitive with high-dimensional data, thus there exists a key need for more efficient JBSS algorithms. JBSS algorithms typically rely on numerical solutions, which may be expensive due to their iterative nature. In contrast, analytic solutions follow consistent procedures that are often less expensive. In this paper, we introduce an efficient analytic solution for JBSS. Denoting a set of sources dependent across the datasets as a “source component vector” (SCV), our solution minimizes correlation among separate SCVs by minimizing distance of the SCV cross-covariance’s eigenvector matrix from a block diagonal matrix. Under the orthogonality constraint, this leads to a system of linear equations wherein each subproblem has an analytic solution. We derive identifiability conditions of our solution’s estimator, and demonstrate estimation performance and time efficiency in comparison with other JBSS algorithms that exploit source correlation across datasets. Results demonstrate that our solution achieves the lowest asymptotic computational complexity among JBSS algorithms, and is capable of superior estimation performance compared with algorithms of similar complexity.
dc.description.sponsorshipThis work was supported in part by NSF-NCS 1631838, NIH grants R01 MH118695, R01 MH123610, R01 AG073949, and German Research Foundation grant SCHR 1384/3-2. The computational hardware used is part of the UMBC High Performance Computing Facility (HPCF), supported by the U.S. NSF through the MRI and SCREMS programs (grants CNS-0821258, CNS-1228778, OAC-1726023, DMS-0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC).
dc.description.urihttps://ieeexplore.ieee.org/abstract/document/10521874/authors#authors
dc.format.extent13 pages
dc.genrejournal articles
dc.genrepostprints
dc.identifierdoi:10.13016/m2jvyg-cim7
dc.identifier.citationGabrielson, Ben, M. A. B. S. Akhonda, Isabell Lehmann, and Tülay Adali. “An Efficient Analytic Solution for Joint Blind Source Separation.” IEEE Transactions on Signal Processing, 2024, 1–13. https://doi.org/10.1109/TSP.2024.3394655.
dc.identifier.urihttps://doi.org/10.1109/TSP.2024.3394655
dc.identifier.urihttp://hdl.handle.net/11603/34335
dc.language.isoen_US
dc.publisherIEEE
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.rights© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectBlind source separation
dc.subjectCorrelation
dc.subjectCovariance matrices
dc.subjectIndependent Vector Analysis
dc.subjectIndexes
dc.subjectJoint Blind Source Separation
dc.subjectMathematical models
dc.subjectMultiset Canonical Correlation Analysis
dc.subjectSignal processing algorithms
dc.subjectVectors
dc.titleAn efficient analytic solution for joint blind source separation
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0001-9217-6641
dcterms.creatorhttps://orcid.org/0000-0003-0594-2796

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
An_efficient_analytic_solution_for_joint_blind_source_separation.pdf
Size:
1.58 MB
Format:
Adobe Portable Document Format
Download

Collections

UMBC Faculty Collection
UMBC Computer Science and Electrical Engineering Department
UMBC Student Collection