Multidataset Independent Subspace Analysis with Application to Multimodal Fusion

Rogers F. Silva, Sergey M. Plis, Tulay Adali, Marios S. Pattichis, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review

Abstract

Unsupervised latent variable models - blind source separation (BSS) especially - enjoy a strong reputation for their interpretability. But they seldom combine the rich diversity of information available in multiple datasets, even though multidatasets yield insightful joint solutions otherwise unavailable in isolation. We present a direct, principled approach to multidataset combination that takes advantage of multidimensional subspace structures. In turn, we extend BSS models to capture the underlying modes of shared and unique variability across and within datasets. Our approach leverages joint information from heterogeneous datasets in a flexible and synergistic fashion. We call this method multidataset independent subspace analysis (MISA). Methodological innovations exploiting the Kotz distribution for subspace modeling, in conjunction with a novel combinatorial optimization for evasion of local minima, enable MISA to produce a robust generalization of independent component analysis (ICA), independent vector analysis (IVA), and independent subspace analysis (ISA) in a single unified model. We highlight the utility of MISA for multimodal information fusion, including sample-poor regimes ( N = 600 ) and low signal-to-noise ratio, promoting novel applications in both unimodal and multimodal brain imaging data.

Original languageEnglish (US)
Article number9217943
Pages (from-to)588-602
Number of pages15
JournalIEEE Transactions on Image Processing
Volume30
DOIs
StatePublished - 2021

Keywords

  • BSS
  • ICA
  • ISA
  • IVA
  • MISA
  • fusion
  • multidataset
  • multimodality
  • multiset data analysis
  • subspace
  • unify
  • unimodal

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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