Interactive Principal Component Analysis

Show simple item record

dc.contributor University of Helsinki, Department of Modern Languages 2010-2017 en
dc.contributor University of Helsinki, Department of Modern Languages 2010-2017 en
dc.contributor.author Siirtola, Harri
dc.contributor.author Säily, Tanja
dc.contributor.author Nevalainen, Terttu
dc.contributor.editor Banissi (et al.), Ebad
dc.date.accessioned 2017-12-21T13:59:01Z
dc.date.available 2017-12-21T13:59:01Z
dc.date.issued 2017
dc.identifier.citation Siirtola , H , Säily , T & Nevalainen , T 2017 , Interactive Principal Component Analysis . in E Banissi (et al.) (ed.) , Proceedings of the 21st International Conference on Information Visualisation (IV 2017) . Information Visualization , IEEE Computer Society , Los Alamitos, California , pp. 416-421 , International Conference on Information Visualisation (IV 2017) , London , United Kingdom , 11/07/2017 . https://doi.org/10.1109/iV.2017.39 en
dc.identifier.citation conference en
dc.identifier.isbn 978-1-5386-0831-9
dc.identifier.other PURE: 85520854
dc.identifier.other PURE UUID: ce08e488-afe5-4d82-a915-a3262122d745
dc.identifier.other Scopus: 85040613075
dc.identifier.other ORCID: /0000-0003-3088-4903/work/39874443
dc.identifier.other ORCID: /0000-0003-4407-8929/work/39925396
dc.identifier.other WOS: 000419271000067
dc.identifier.uri http://hdl.handle.net/10138/229861
dc.description.abstract Principal Component Analysis (PCA) is an established and efficient method for finding structure in a multidimensional data set. PCA is based on orthogonal transformations that convert a set of multidimensional values into linearly uncorrelated variables called principal components.The main disadvantage to the PCA approach is that the procedure and outcome are often difficult to understand. The connection between input and output can be puzzling, a small change in input can yield a completely different output, and the user may often wonder if the PCA is doing the right thing.We introduce a user interface that makes the procedure and result easier to understand. We have implemented an interactive PCA view in our text visualization tool called Text Variation Explorer. It allows the user to interactively study the result of PCA, and provides a better understanding of the process.We believe that although we are addressing the problem of interactive principal component analysis in the context of text visualization, these ideas should be useful in other contexts as well. en
dc.format.extent 6
dc.language.iso eng
dc.publisher IEEE Computer Society
dc.relation.ispartof Proceedings of the 21st International Conference on Information Visualisation (IV 2017)
dc.relation.ispartofseries Information Visualization
dc.rights en
dc.subject 6121 Languages en
dc.subject text visualization en
dc.subject corpus linguistics en
dc.subject historical sociolinguistics en
dc.subject 113 Computer and information sciences en
dc.subject information visualization en
dc.subject principal component analysis en
dc.subject interactive visualization en
dc.title Interactive Principal Component Analysis en
dc.type Conference contribution
dc.identifier.doi https://doi.org/10.1109/iV.2017.39
dc.type.uri info:eu-repo/semantics/other
dc.contributor.pbl

Files in this item

Total number of downloads: Loading...

Files Size Format View
siirtola_et_al_2017_accepted.pdf 1.582Mb PDF View/Open

This item appears in the following Collection(s)

Show simple item record