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dc.contributor.authorRadojičić, Una
dc.contributor.authorLietzén, Niko
dc.contributor.authorNordhausen, Klaus
dc.contributor.authorVirta, Joni
dc.contributor.editorPetkovié, T.
dc.contributor.editorPetrinovié, D.
dc.contributor.editorLonéarié, S.
dc.date.accessioned2021-11-15T12:53:39Z
dc.date.available2021-11-15T12:53:39Z
dc.date.issued2021
dc.identifier.citationRadojičić, U., Lietzén, N., Nordhausen, K., & Virta, J. (2021). Dimension Estimation in Two-Dimensional PCA. In T. Petkovié, D. Petrinovié, & S. Lonéarié (Eds.), <i>Proceedings of the 12th International Symposium on Image and Signal Processing and Analysis (ISPA 2021)</i>. IEEE; University of Zagreb. International Symposium on Image and Signal Processing and Analysis. <a href="https://doi.org/10.1109/ispa52656.2021.9552114" target="_blank">https://doi.org/10.1109/ispa52656.2021.9552114</a>
dc.identifier.otherCONVID_101409122
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/78658
dc.description.abstractWe propose an automated way of determining the optimal number of low-rank components in dimension reduction of image data. The method is based on the combination of two-dimensional principal component analysis and an augmentation estimator proposed recently in the literature. Intuitively, the main idea is to combine a scree plot with information extracted from the eigenvectors of a variation matrix. Simulation studies show that the method provides accurate estimates and a demonstration with a finger data set showcases its performance in practice.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherIEEE; University of Zagreb
dc.relation.ispartofProceedings of the 12th International Symposium on Image and Signal Processing and Analysis (ISPA 2021)
dc.relation.ispartofseriesInternational Symposium on Image and Signal Processing and Analysis
dc.rightsIn Copyright
dc.subject.otheraugmentation
dc.subject.otherdimension estimation
dc.subject.otherdimension reduction
dc.subject.otherimage data
dc.subject.otherscree plot
dc.titleDimension Estimation in Two-Dimensional PCA
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202111155671
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-1-6654-2639-8
dc.description.reviewstatuspeerReviewed
dc.relation.issn1845-5921
dc.type.versionacceptedVersion
dc.rights.copyright© 2021 IEEE
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Symposium on Image and Signal Processing and Analysis
dc.format.contentfulltext
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1109/ispa52656.2021.9552114
jyx.fundinginformationThe work of NL was supported by the Academy of Finland (Grant 321968). The work of JV was supported by the Academy of Finland (Grant 335077).


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