IVA using complex multivariate GGD: application to fMRI analysis

Volume: 31, Issue: 2, Pages: 725 - 744
Published: Oct 9, 2019
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...
Paper Details
Title
IVA using complex multivariate GGD: application to fMRI analysis
Published Date
Oct 9, 2019
Volume
31
Issue
2
Pages
725 - 744
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.