Evidence map›Paper›PMID 40994814›Full record

ArticleIEEE transactions on signal processing : a publication of the IEEE Signal Processing Society2025

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

Ben Gabrielson, Hanlu Yang, Trung Vu, Vince Calhoun, Tülay Adali

Abstract read
In one paragraph

Article in IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ben GabrielsonDepartment of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore MD.
Hanlu YangDepartment of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore MD.
Trung VuDepartment of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore MD.
Vince CalhounTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia.
Tülay AdaliDepartment of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore MD.

Funding

Male/Female differences in psychosis and mood disorders:Dynamic imaging-genomic models for characterizing and predicting psychosis and mood dR01MH118695 · NIMH · GEORGIA STATE UNIVERSITY · PI ADALI, TULAY, CALHOUN, VINCE D · 2019 to 2023
$3.8M
Data driven dynamic activity/connectivity methods for early detection of Alzheimer’sR01AG073949 · NIA · GEORGIA STATE UNIVERSITY · PI ADALI, TULAY, CALHOUN, VINCE D · 2021 to 2025
$3.7M
Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivityR01MH123610 · NIMH · GEORGIA STATE UNIVERSITY · PI ADALI, TULAY, CALHOUN, VINCE D · 2021 to 2025
$3.1M
NIA NIH HHS R01 AG073949NIMH NIH HHS R01 MH118695NIMH NIH HHS R01 MH123610
6 · The paper itself

Abstract

Joint blind source separation (JBSS) involves the factorization of multiple matrices, i.e. "datasets", into "sources" that are statistically dependent across datasets and independent within datasets. Despite this usefulness for analyzing multiple datasets, JBSS methods suffer from considerable computational costs and are typically intractable for hundreds or thousands of datasets. To address this issue, we present a methodology for how a subset of the datasets can be used to perform efficient JBSS over the full set. We motivate two such methods: a numerical extension of independent vector analysis (IVA) with the multivariate Gaussian model (IVA-G), and a recently proposed analytic method resembling generalized joint diagonalization (GJD). We derive nonidentifiability conditions for both methods, and then demonstrate how one can significantly improve these methods' generalizability by an efficient representative subset selection method. This involves selecting a

Indexed as

Independent Vector AnalysisJoint Blind Source SeparationMultiset Canonical Correlation Analysis

Identifiers

PMID40994814
PMCPMC12456423

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.