Classification methods for multivariate functional data with applications to biomedical signal

Andrea Ghiglietti, Andrea Martino, Anna M. Paganoni

Risultato della ricerca: Contributo in libroContributo a convegno

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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteClassification and Data Analysis Group : Book of abstracts
Pagine1-6
Numero di pagine6
Stato di pubblicazionePubblicato - 2017
EventoConference of the CLAssification and Data Analysis Group (CLADAG) - Milano
Durata: 13 set 201715 set 2017

Convegno

ConvegnoConference of the CLAssification and Data Analysis Group (CLADAG)
CittàMilano
Periodo13/9/1715/9/17

Keywords

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

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