2024
Mode Coresets for Efficient, Interpretable Tensor Decompositions: An Application to Feature Selection in fMRI Analysis
Gabrielson B, Yang H, Vu T, Calhoun V, Adali T. Mode Coresets for Efficient, Interpretable Tensor Decompositions: An Application to Feature Selection in fMRI Analysis. IEEE Access 2024, 12: 192356-192376. DOI: 10.1109/access.2024.3517338.Peer-Reviewed Original ResearchTensor decompositionSize of modern datasetsRank-1 tensorsComputational complexity scalesCore tensorTucker decompositionFeature selectionComputational complexitySelection schemeData tensorMultidimensional arraysRank-1CoresetTensor dataMatrix decompositionModern datasetsMassive sizeMyriad of applicationsMethod efficiencyDatasetSelection abilityComplexity scalesMeasure of discrepancyWell-approximatedDecomposition method
2023
Coupled CP Tensor Decomposition with Shared and Distinct Components for Multi-Task Fmri Data Fusion
Borsoi R, Lehmann I, Akhonda M, Calhoun V, Usevich K, Brie D, Adali T. Coupled CP Tensor Decomposition with Shared and Distinct Components for Multi-Task Fmri Data Fusion. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10096241.Peer-Reviewed Original ResearchCP tensor decompositionTensor factorization approachDataset-specific featuresTensor-based frameworkPost-processing stepExtract featuresFunctional magnetic resonance imagingHyperparameter selectionTensor decompositionData fusionMulti-taskingDiscover componentsMultiple datasetsTaskCoupling matrixFunctional magnetic resonance imaging dataHyperparametersDatasetFeaturesGroup differencesFactor approachDecompositionFusion
2022
Characterizing Spatiotemporal Transcriptome of the Human Brain Via Low-Rank Tensor Decomposition
Liu T, Yuan M, Zhao H. Characterizing Spatiotemporal Transcriptome of the Human Brain Via Low-Rank Tensor Decomposition. Statistics In Biosciences 2022, 14: 485-513. DOI: 10.1007/s12561-021-09331-5.Peer-Reviewed Original ResearchLow-rank tensor decompositionTensor decompositionPower iterationClassical principal component analysisStatistical performanceNumerical experimentsTensor unfoldingStatistical methodsGene expression dataEfficient algorithmData matrixExpression dataTensor principal componentsBrain expression dataPrincipal component analysisIterationDecompositionSpatiotemporal transcriptomeImplicit assumptionAlgorithmDynamicsTrajectoriesGuaranteesAssumptionSpatial patterns
2015
Trends in biomedical informatics: automated topic analysis of JAMIA articles
Han D, Wang S, Jiang C, Jiang X, Kim H, Sun J, Ohno-Machado L. Trends in biomedical informatics: automated topic analysis of JAMIA articles. Journal Of The American Medical Informatics Association 2015, 22: 1153-1163. PMID: 26555018, PMCID: PMC5009912, DOI: 10.1093/jamia/ocv157.Peer-Reviewed Original ResearchConceptsTensor decomposition
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