English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The classification of multivariate functional data is an important task inscientific research. Unlike point-wise data, functional data are usuallyclassified by their shapes rather than by their scales. We define anoutlyingness matrix by extending directional outlyingness, an effective measureof the shape variation of curves that combines the direction of outlyingnesswith conventional depth. We propose two classifiers based on directionaloutlyingness and the outlyingness matrix, respectively. Our classifiers providebetter performance compared with existing depth-based classifiers when appliedon both univariate and multivariate functional data from simulation studies. Wealso test our methods on two data problems: speech recognition and gestureclassification, and obtain results that are consistent with the findings fromthe simulated data.

An Outlyingness Matrix for Multivariate Functional Data Classification