Multimodal Data Fusion Using Source Separation: Two Effective Models Based on ICA and IVA and Their Properties

• Published in: Proceedings of the IEEE Vol. 103; no. 9; pp. 1478 - 1493
• Main Authors: AdalI, Tulay, Levin-Schwartz, Yuri, Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: (joint) blind source separation; Brain models; Computer simulation; Correlation; Data fusion; Data integration; Data models; Datasets; Decomposition; Feature extraction; independent component analysis (ICA); independent vector analysis (IVA); Joints; Mathematical models; multimodality; Source separation; Tasks; Vector analysis; Data fusion; multimodality; independent component analysis (ICA); independent vector analysis (IVA); (joint) blind source separation

Fusion of information from multiple sets of data in order to extract a set of features that are most useful and relevant for the given task is inherent to many problems we deal with today. Since, usually, very little is known about the actual interaction among the data sets, it is highly desirable t...