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Mode Coresets for Efficient, Interpretable Tensor Decompositions: An Application to Feature Selection in fMRI Analysis

IEEE Access(2024)10.1109/access.2024.3517338Source: DataRank Database

Mode Coresets for Efficient, Interpretable Tensor Decompositions: An Application to Feature Selection in fMRI Analysis is a research paper published in IEEE Access (2024). On theSindex it has a DataRank of 0. It has been cited 2 times.

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (15 grants)Impact (FWCI 0.46)OA: goldTopics (36)IDs (PubMed, PMC, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (5)

Hanlu Yang ORCID,Trung Vu ORCID,Vince D. Calhoun ORCID,Tülay Adalı ORCID,Ben Gabrielson ORCID

Live enrichment

PubMed:40822667 PMC:PMC12352452 OpenAlex:https://openalex.org/W4411472170

NSF

Grant: 2316420

Collaborative Research:CISE-ANR:CIF:Small:Learning from Large Datasets - Application to Multi-Subject fMRI Analysis

NIH

Grant: R01MH118695

NIH

Grant: R01MH123610

NIH

Grant: R01AG073949

Computational Hardware used is part of the University of Maryland, Baltimore County (UMBC) High Performance Computing Facility (HPCF) funded by the U.S. NSF through the MRI and SCREMS Programs

Grant: CNS-0821258

Computational Hardware used is part of the University of Maryland, Baltimore County (UMBC) High Performance Computing Facility (HPCF) funded by the U.S. NSF through the MRI and SCREMS Programs

Grant: CNS-1228778

Computational Hardware used is part of the University of Maryland, Baltimore County (UMBC) High Performance Computing Facility (HPCF) funded by the U.S. NSF through the MRI and SCREMS Programs

Grant: OAC-1726023

Computational Hardware used is part of the University of Maryland, Baltimore County (UMBC) High Performance Computing Facility (HPCF) funded by the U.S. NSF through the MRI and SCREMS Programs

Grant: DMS-0821311

National Science Foundation

Grant: 1726023

MRI: Acquisition of Cutting-Edge GPU and Phi Nodes for the Interdisciplinary UMBC High Performance Computing Facility

National Institutes of Health

Grant: 1R01AG073949-01

Data driven dynamic activity/connectivity methods for early detection of Alzheimer’s

National Institutes of Health

Grant: 1R01MH123610-01A1

Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivity

National Institutes of Health

Grant: 3R01MH118695-03S1

Male/Female differences in psychosis and mood disorders:Dynamic imaging-genomic models for characterizing and predicting psychosis and mood d

National Science Foundation

Grant: 0821258

MRI: Acquisition of an Interdisciplinary Facility for High-Performance Computing

National Science Foundation

Grant: 1228778

MRI: Acquisition of Hybrid CPU/GPU Nodes for the Interdisciplinary UMBC High Performance Computing Facility

National Science Foundation

Grant: 0821311

SCREMS: Parallel Computing for Interdisciplinary Research in Mathematics and Statistics

FWCI

0.46

Citation Percentile

0.6%

Citation Trend

2026

goldLicense: cc-by

journalhttps://doi.org/10.1109/access.2024.3517338

publisherhttps://doi.org/10.1109/access.2024.3517338

publisherhttps://ieeexplore.ieee.org/ielam/6287639/10380310/10798430-aam.pdf

publisherhttp://xplorestaging.ieee.org/ielx8/6287639/10380310/10798430.pdf?arnumber=10798430

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40822667

Fields of Study

Tensor decomposition and applicationsModel Reduction and Neural NetworksAdvanced Neuroimaging Techniques and Applications02 engineering and technology0202 electrical engineering, electronic engineering, information engineering

Keywords

Computer sciencePattern recognition (psychology)Tensor (intrinsic definition)Feature selectionSelection (genetic algorithm)Artificial intelligenceMode (computer interface)Feature extractionFeature (linguistics)MathematicsfMRISubset SelectionTensor DecompositionTucker DecompositionCoresetsTensor Cur DecompositionHigher Order Singular Value DecompositionOptimizationFunctional magnetic resonance imagingSingular value decompositionTensorsVectorsArticleTK1-9971CostsComputational complexityUMBC Machine Learning and Signal Processing Lab (MLSP-Lab)Matrix decompositionMatrix convertersElectrical engineering. Electronics. Nuclear engineeringUMBC Ebiquity Research Group

Sustainable Development Goals

SDG 0: Climate action