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dc.contributor.authorGao, Xu
dc.contributor.authorShen, Weining
dc.contributor.authorZhang, Liwen
dc.contributor.authorHu, Jianhua
dc.contributor.authorFortin, Norbert J.
dc.contributor.authorFrostig, Ron D.
dc.contributor.authorOmbao, Hernando
dc.date.accessioned2020-08-17T13:13:22Z
dc.date.available2020-08-17T13:13:22Z
dc.date.issued2020-08-24
dc.date.submitted2019-08-14
dc.identifier.citationGao, X., Shen, W., Zhang, L., Hu, J., Fortin, N. J., Frostig, R. D., & Ombao, H. (2020). Regularized matrix data clustering and its application to image analysis. Biometrics. doi:10.1111/biom.13354
dc.identifier.issn0006-341X
dc.identifier.issn1541-0420
dc.identifier.doi10.1111/biom.13354
dc.identifier.urihttp://hdl.handle.net/10754/664640
dc.description.abstractWe propose a novel regularized mixture model for clustering matrix-valued data. The proposed method assumes a separable covariance structure for each cluster and imposes a sparsity structure (e.g., low rankness, spatial sparsity) for the mean signal of each cluster. We formulate the problem as a finite mixture model of matrix-normal distributions with regularization terms, and then develop an EM-type of algorithm for efficient computation. In theory, we show that the proposed estimators are strongly consistent for various choices of penalty functions. Simulation and two applications on brain signal studies confirm the excellent performance of the proposed method including a better prediction accuracy than the competitors and the scientific interpretability of the solution.
dc.description.sponsorshipThe authors thank the editor, the associate editor and two referees for their constructive and helpful comments on the earlier version of this paper. Shen’s research was partially supported by the Simons Foundation Award 512620 and the NSF Grant DMS 1509023. Hu’s effort was partially supported by the National Institute of Health Grants R01AI143886, R01CA219896,and CCSG P30 CA013696. Fortin’s research was supported by NIH grant R01-MH115697,NSF awards IOS-1150292 and BCS-1439267, and Whitehall Foundation award 2010-05-84.Frostig’s research was partially supported by the Leducq Foundation.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13354
dc.rightsArchived with thanks to Biometrics
dc.titleRegularized matrix data clustering and its application to image analysis
dc.typeArticle
dc.contributor.departmentBiostatistics Group
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalBiometrics
dc.rights.embargodate2021-07-21
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of StatisticsUniversity of CaliforniaIrvine California U.S.A.
dc.contributor.institutionShanghai University of Finance and EconomicsShanghai China
dc.contributor.institutionHerbert Irving Comprehensive Cancer CenterColumbia UniversityNew York U.S.A.
dc.contributor.institutionDepartment of Neurobiology and BehaviorUniversity of CaliforniaIrvine California U.S.A.
dc.contributor.institutionDepartment of Biomedical EngineeringUniversity of CaliforniaIrvine California U.S.A.
dc.identifier.arxivid1808.01749
kaust.personOmbao, Hernando
dc.date.accepted2020-07-21
refterms.dateFOA2020-08-17T13:14:08Z


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