Linear discriminant analysis for multiple functional data analysis

Sugnet Gardner-Lubbe

Journal of Applied Statistics, 2021, vol. 48, issue 11, 1917-1933

Abstract: In multivariate data analysis, Fisher linear discriminant analysis is useful to optimally separate two classes of observations by finding a linear combination of p variables. Functional data analysis deals with the analysis of continuous functions and thus can be seen as a generalisation of multivariate analysis where the dimension of the analysis space p strives to infinity. Several authors propose methods to perform discriminant analysis in this infinite dimensional space. Here, the methodology is introduced to perform discriminant analysis, not on single infinite dimensional functions, but to find a linear combination of p infinite dimensional continuous functions, providing a set of continuous canonical functions which are optimally separated in the canonical space.

Date: 2021
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DOI: 10.1080/02664763.2020.1780569

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