IVA using complex multivariate GGD: application to fMRI analysis
| dc.contributor.author | Mowakeaa, Rami | |
| dc.contributor.author | Boukouvalas, Zois | |
| dc.contributor.author | Long, Qunfang | |
| dc.contributor.author | Adali, Tulay | |
| dc.date.accessioned | 2020-09-10T17:41:27Z | |
| dc.date.available | 2020-09-10T17:41:27Z | |
| dc.date.issued | 2019-10-09 | |
| dc.description.abstract | Examples of complex-valued random phenomena in science and engineering are abound, and joint blind source separation (JBSS) provides an effective way to analyze multiset data. Thus there is a need for flexible JBSS algorithms for efficient data-driven feature extraction in the complex domain. Independent vector analysis (IVA) is a prominent recent extension of independent component analysis to multivariate sources, i.e., to perform JBSS, but its effectiveness is determined by how well the source models used match the true latent distributions and the optimization algorithm employed. The complex multivariate generalized Gaussian distribution (CMGGD) is a simple, yet effective parameterized family of distributions that account for full second- and higher-order statistics including noncircularity, a property that has been often omitted for convenience. In this paper, we marry IVA and CMGGD to derive, IVA-CMGGD, with a number of numerical optimization implementations including steepest descent, the quasi-Newton method Broyden–Fletcher–Goldfarb–Shanno (BFGS), and its limited-memory sibling limited-memory BFGS all in the complex-domain. We demonstrate the performance of our algorithm on simulated data as well as a 14-subject real-world complex-valued functional magnetic resonance imaging dataset against a number of competing algorithms. | en_US |
| dc.description.sponsorship | The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF). The facility is supported by the U.S. National Science Foundation through the MRI program (Grant Nos. CNS-0821258, CNS-1228778, OAC-1726023, 1618551 and 1631838) and the SCREMS program (Grant No. DMS-0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the Projects using its resources. This work was supported in part by NSF-CCF 1618551 and NSF-NCS 1631838. | en_US |
| dc.description.uri | https://link.springer.com/article/10.1007/s11045-019-00685-0 | en_US |
| dc.format.extent | 22 pages | en_US |
| dc.genre | journal articles postprints | en_US |
| dc.identifier | doi:10.13016/m2gt4t-eajw | |
| dc.identifier.citation | Mowakeaa, Rami; Boukouvalas, Zois; Long, Qunfang; Adali, Tülay; IVA using complex multivariate GGD: application to fMRI analysis; Multidimensional Systems and Signal Processing volume 31, pages725–744(2020); https://link.springer.com/article/10.1007/s11045-019-00685-0 | en_US |
| dc.identifier.uri | https://doi.org/10.1007/s11045-019-00685-0 | |
| dc.identifier.uri | http://hdl.handle.net/11603/19633 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Springer Nature Switzerland AG. | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This 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 | Access to this item will begin on 2020-10-09 | |
| dc.subject | UMBC Machine Learning for Signal Processing Lab | en_US |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | |
| dc.title | IVA using complex multivariate GGD: application to fMRI analysis | en_US |
| dc.type | Text | en_US |