dc.contributor.author | Radojičić, Una | |
dc.contributor.author | Lietzén, Niko | |
dc.contributor.author | Nordhausen, Klaus | |
dc.contributor.author | Virta, Joni | |
dc.contributor.editor | Petkovié, T. | |
dc.contributor.editor | Petrinovié, D. | |
dc.contributor.editor | Lonéarié, S. | |
dc.date.accessioned | 2021-11-15T12:53:39Z | |
dc.date.available | 2021-11-15T12:53:39Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Radojičić, 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.other | CONVID_101409122 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/78658 | |
dc.description.abstract | We 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.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | IEEE; University of Zagreb | |
dc.relation.ispartof | Proceedings of the 12th International Symposium on Image and Signal Processing and Analysis (ISPA 2021) | |
dc.relation.ispartofseries | International Symposium on Image and Signal Processing and Analysis | |
dc.rights | In Copyright | |
dc.subject.other | augmentation | |
dc.subject.other | dimension estimation | |
dc.subject.other | dimension reduction | |
dc.subject.other | image data | |
dc.subject.other | scree plot | |
dc.title | Dimension Estimation in Two-Dimensional PCA | |
dc.type | conferenceObject | |
dc.identifier.urn | URN:NBN:fi:jyu-202111155671 | |
dc.contributor.laitos | Matematiikan ja tilastotieteen laitos | fi |
dc.contributor.laitos | Department of Mathematics and Statistics | en |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
dc.relation.isbn | 978-1-6654-2639-8 | |
dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
dc.description.reviewstatus | peerReviewed | |
dc.relation.issn | 1845-5921 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © 2021 IEEE | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.conference | International Symposium on Image and Signal Processing and Analysis | |
dc.format.content | fulltext | |
dc.rights.url | http://rightsstatements.org/page/InC/1.0/?language=en | |
dc.relation.doi | 10.1109/ispa52656.2021.9552114 | |
jyx.fundinginformation | The 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). | |
dc.type.okm | A4 | |