An Outlyingness Matrix for Multivariate Functional Data Classification

Handle URI:
http://hdl.handle.net/10754/626367
Title:
An Outlyingness Matrix for Multivariate Functional Data Classification
Authors:
Dai, Wenlin; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
The classification of multivariate functional data is an important task in scientific research. Unlike point-wise data, functional data are usually classified by their shapes rather than by their scales. We define an outlyingness matrix by extending directional outlyingness, an effective measure of the shape variation of curves that combines the direction of outlyingness with conventional statistical depth. We propose two classifiers based on directional outlyingness and the outlyingness matrix, respectively. Our classifiers provide better performance compared with existing depth-based classifiers when applied on both univariate and multivariate functional data from simulation studies. We also test our methods on two data problems: speech recognition and gesture classification, and obtain results that are consistent with the findings from the simulated data.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Dai W, Genton MG (2018) An Outlyingness Matrix for Multivariate Functional Data Classification. Statistica Sinica. Available: http://dx.doi.org/10.5705/ss.202016.0537.
Publisher:
Institute of Statistical Science
Journal:
Statistica Sinica
Issue Date:
25-Aug-2017
DOI:
10.5705/ss.202016.0537
Type:
Article
ISSN:
1017-0405
Sponsors:
The authors thank the editor, the associate editor and the two referees for their constructive comments that led to a substantial improvement of the paper. The work of Wenlin Dai and Marc G. Genton was supported by King Abdullah University of Science and Technology (KAUST).
Additional Links:
http://www3.stat.sinica.edu.tw/ss_newpaper/SS-2016-0537_na.pdf
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorDai, Wenlinen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2017-12-14T12:34:03Z-
dc.date.available2017-12-14T12:34:03Z-
dc.date.issued2017-08-25en
dc.identifier.citationDai W, Genton MG (2018) An Outlyingness Matrix for Multivariate Functional Data Classification. Statistica Sinica. Available: http://dx.doi.org/10.5705/ss.202016.0537.en
dc.identifier.issn1017-0405en
dc.identifier.doi10.5705/ss.202016.0537en
dc.identifier.urihttp://hdl.handle.net/10754/626367-
dc.description.abstractThe classification of multivariate functional data is an important task in scientific research. Unlike point-wise data, functional data are usually classified by their shapes rather than by their scales. We define an outlyingness matrix by extending directional outlyingness, an effective measure of the shape variation of curves that combines the direction of outlyingness with conventional statistical depth. We propose two classifiers based on directional outlyingness and the outlyingness matrix, respectively. Our classifiers provide better performance compared with existing depth-based classifiers when applied on both univariate and multivariate functional data from simulation studies. We also test our methods on two data problems: speech recognition and gesture classification, and obtain results that are consistent with the findings from the simulated data.en
dc.description.sponsorshipThe authors thank the editor, the associate editor and the two referees for their constructive comments that led to a substantial improvement of the paper. The work of Wenlin Dai and Marc G. Genton was supported by King Abdullah University of Science and Technology (KAUST).en
dc.publisherInstitute of Statistical Scienceen
dc.relation.urlhttp://www3.stat.sinica.edu.tw/ss_newpaper/SS-2016-0537_na.pdfen
dc.rightsArchived with thanks to Statistica Sinicaen
dc.subjectDirectional outlyingnessen
dc.subjectFunctional data classificationen
dc.subjectMultivariate functional dataen
dc.subjectOutlyingness matrixen
dc.subjectStatistical depthen
dc.titleAn Outlyingness Matrix for Multivariate Functional Data Classificationen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalStatistica Sinicaen
dc.eprint.versionPost-printen
kaust.authorDai, Wenlinen
kaust.authorGenton, Marc G.en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.