Independent component analysis for multivariate functional data

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Tutkijat

  • Joni Virta
  • Bing Li
  • Klaus Nordhausen
  • Hannu Oja

Organisaatiot

  • University of Turku
  • Pennsylvania State University
  • Vienna University of Technology

Kuvaus

We extend two methods of independent component analysis, fourth order blind identification and joint approximate diagonalization of eigen-matrices, to vector-valued functional data. Multivariate functional data occur naturally and frequently in modern applications, and extending independent component analysis to this setting allows us to distill important information from this type of data, going a step further than the functional principal component analysis. To allow the inversion of the covariance operator we make the assumption that the dependency between the component functions lies in a finite-dimensional subspace. In this subspace we define fourth cross-cumulant operators and use them to construct the two novel, Fisher consistent methods for solving the independent component problem for vector-valued functions. Both simulations and an application on a hand gesture data set show the usefulness and advantages of the proposed methods over functional principal component analysis.

Yksityiskohdat

AlkuperäiskieliEnglanti
Artikkeli104568
JulkaisuJournal of Multivariate Analysis
Vuosikerta176
TilaJulkaistu - 1 maaliskuuta 2020
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

ID: 40653720