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Large-Scale Independent Vector Analysis (IVA-G) via Coresets.

Ben Gabrielson1, Hanlu Yang1, Trung Vu1

  • 1Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore MD.

IEEE Transactions on Signal Processing : a Publication of the IEEE Signal Processing Society
|September 25, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel method for efficient joint blind source separation (JBSS) using a representative data subset, significantly improving scalability for large datasets like fMRI.

Keywords:
Independent Vector AnalysisJoint Blind Source SeparationMultiset Canonical Correlation Analysis

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Neuroimaging Analysis

Background:

  • Joint blind source separation (JBSS) analyzes multiple datasets by factorizing them into statistically dependent sources.
  • Existing JBSS methods face computational challenges, limiting their application to large numbers of datasets.

Purpose of the Study:

  • To develop an efficient JBSS methodology applicable to a large number of datasets.
  • To improve the scalability and generalizability of JBSS techniques.

Main Methods:

  • Proposed a coreset selection method to identify a representative subset of datasets for efficient JBSS.
  • Investigated two JBSS methods: an extension of independent vector analysis with a Gaussian model (IVA-G) and generalized joint diagonalization (GJD).
  • Derived nonidentifiability conditions and applied the coreset method to enhance generalizability.

Main Results:

  • The proposed 'coreIVA-G' method demonstrated significant scalability advantages over existing JBSS methods.
  • Achieved superior source separation performance on simulated and real functional magnetic resonance imaging (fMRI) data.
  • The coreset approach effectively minimized discrepancy between subset and full dataset statistics.

Conclusions:

  • Efficient JBSS is achievable by utilizing a representative data subset (coreset).
  • The coreIVA-G method offers a scalable and effective solution for analyzing large-scale multi-dataset problems, particularly in neuroimaging.
  • This approach overcomes the computational intractability of traditional JBSS methods for numerous datasets.