Multidataset independent subspace analysis extends independent vector analysis

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Despite its multivariate nature, independent component analysis (ICA) is generally limited to univariate latents in the sense that each latent component is a scalar process. Independent subspace analysis (ISA), or multidimensional ICA (MICA), is a generalization of ICA which identifies latent independent vector components instead. While ISA/MICA considers multidimensional latent components within a single dataset, our work specifically considers the case of multiple datasets. Independent vector analysis (IVA) is a related technique that also considers multiple datasets explicitly but with a fixed and constrained model. Here, we first show that 1) ISA/MICA naturally extends to the case of multiple datasets (which we call MISA), and that 2) IVA is a special case of this extension. Then we develop an algorithm for MISA and demonstrate its performance on both IVA- and MISA-type problems. The benefit of these extensions is that the vector sources (or subspaces) capture higher order statistical dependence across datasets while retaining independence between subspaces. This is a promising model that can explore complex latent relations across multiple datasets and help identify novel biological traits for intricate mental illnesses such as schizophrenia.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781479957514
StatePublished - Jan 28 2014
Externally publishedYes

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014


  • ICA
  • ISA
  • IVA
  • MICA
  • MISA
  • multidataset
  • multimodal

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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