An Outlyingness Matrix for Multivariate Functional Data Classification
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2018
Print Publication Date2018
Permanent link to this recordhttp://hdl.handle.net/10754/626367
MetadataShow full item record
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.
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.
SponsorsThe 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).