| dc.contributor.author | Kärkkäinen, Tommi | |
| dc.contributor.author | Saarela, Mirka | |
| dc.contributor.editor | Perner, Petra | |
| dc.date.accessioned | 2015-09-07T06:03:33Z | |
| dc.date.available | 2016-07-01T21:45:05Z | |
| dc.date.issued | 2015 | |
| dc.identifier.citation | Kärkkäinen, T., & Saarela, M. (2015). Robust Principal Component Analysis of Data with Missing Values. In P. Perner (Ed.), <i>Machine Learning and Data Mining in Pattern Recognition : Proceedings of the 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015</i> (pp. 140-154). Springer International Publishing. Lecture Notes in Computer Science, 9166. <a href="https://doi.org/10.1007/978-3-319-21024-7_10" target="_blank">https://doi.org/10.1007/978-3-319-21024-7_10</a> | |
| dc.identifier.other | CONVID_24832083 | |
| dc.identifier.other | TUTKAID_66827 | |
| dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/46769 | |
| dc.description.abstract | Principal component analysis is one of the most popular machine
learning and data mining techniques. Having its origins in statistics,
principal component analysis is used in numerous applications. However,
there seems to be not much systematic testing and assessment of principal
component analysis for cases with erroneous and incomplete data.
The purpose of this article is to propose multiple robust approaches for
carrying out principal component analysis and, especially, to estimate
the relative importances of the principal components to explain the data
variability. Computational experiments are first focused on carefully designed
simulated tests where the ground truth is known and can be used
to assess the accuracy of the results of the different methods. In addition,
a practical application and evaluation of the methods for an educational
data set is given. | fi |
| dc.format.extent | 454 | |
| dc.language.iso | eng | |
| dc.publisher | Springer International Publishing | |
| dc.relation.ispartof | Machine Learning and Data Mining in Pattern Recognition : Proceedings of the 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015 | |
| dc.relation.ispartofseries | Lecture Notes in Computer Science | |
| dc.subject.other | PCA | |
| dc.subject.other | missing data | |
| dc.subject.other | robust statistics | |
| dc.title | Robust Principal Component Analysis of Data with Missing Values | |
| dc.type | conferenceObject | |
| dc.identifier.urn | URN:NBN:fi:jyu-201509042808 | |
| dc.contributor.laitos | Tietotekniikan laitos | fi |
| dc.contributor.laitos | Department of Mathematical Information Technology | en |
| dc.contributor.oppiaine | Tietotekniikka | fi |
| dc.contributor.oppiaine | Mathematical Information Technology | en |
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
| dc.date.updated | 2015-09-04T06:15:02Z | |
| dc.relation.isbn | 978-3-319-21024-7 | |
| jyx.note | Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 9166) | |
| dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
| dc.description.reviewstatus | peerReviewed | |
| dc.format.pagerange | 140-154 | |
| dc.type.version | acceptedVersion | |
| dc.rights.copyright | © Springer International Publishing Switzerland 2015. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher. | |
| dc.rights.accesslevel | openAccess | fi |
| dc.relation.conference | International conference on machine learning and data mining | |
| dc.relation.doi | 10.1007/978-3-319-21024-7_10 | |
| dc.type.okm | A4 | |
