Classification methods for multivariate functional data with applications to biomedical signal

Andrea Martino, Andrea Ghiglietti, Anna M. Paganoni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work we propose a multivariate functional clustering technique based on a distance which generalize Mahalanobis distance to functional data generated by stochastic processes. This new mathematical tool is well defined in L2(I), where I is a compact interval of R, and considers all the infinite components of data basis expansion while keeping the same ideas on which Mahalanobis distance is based. To test the robustness of our clustering procedure we first present some simulations, comparing the performances obtained using our distance and other known distances, eventually applying it to a dataset of reconstructed and registered ECGs.
Original languageEnglish
Title of host publicationClassification and Data Analysis Group : Book of abstracts
Pages1-6
Number of pages6
Publication statusPublished - 2017
Externally publishedYes
EventConference of the CLAssification and Data Analysis Group (CLADAG) - Milano
Duration: 13 Sept 201715 Sept 2017

Conference

ConferenceConference of the CLAssification and Data Analysis Group (CLADAG)
CityMilano
Period13/9/1715/9/17

Keywords

  • Distances in L2
  • ECG signals
  • Functional Clustering
  • Functional Data

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