Multimodal Data Fusion Using Source Separation: Two Effective Models Based on ICA and IVA and Their Properties
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2015-09
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Tulay Adali, Yuri Levin-Schwartz, Vince D. Calhoun, Multimodal Data Fusion Using Source Separation: Two Effective Models Based on ICA and IVA and Their Properties, Proceedings of the IEEE ( Volume: 103 , Issue: 9 , Sept. 2015 ), DOI: 10.1109/JPROC.2015.2461624
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©2015 IEEE
©2015 IEEE
Abstract
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 to minimize
the underlying assumptions. This has been the main reason
for the growing importance of data-driven methods, and in
particular of independent component analysis (ICA) as it provides
useful decompositions with a simple generative model
and using only the assumption of statistical independence. A
recent extension of ICA, independent vector analysis (IVA),
generalizes ICA to multiple data sets by exploiting the statistical
dependence across the data sets, and hence, as we discuss
in this paper, provides an attractive solution to fusion of data
from multiple data sets along with ICA. In this paper, we focus
on two multivariate solutions for multimodal data fusion that
let multiple modalities fully interact for the estimation of
underlying features that jointly report on all modalities. One
solution is the joint ICA model that has found wide application
in medical imaging, and the second one is the transposed IVA
model introduced here as a generalization of an approach