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cfda: an R Package for Categorical Functional Data Analysis

Cristian Preda 1, 2 Quentin Grimonprez 1 Vincent Vandewalle 3, 1 
1 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
Abstract : Categorical functional data represented by paths of a stochastic jump process with continuous time and finite set of states are considered. As an extension of the multiple correspondence analysis to an infinite set of variables, optimal encodings of states over time are approximated using an arbitrary finite basis of functions. That allows dimension reduction, optimal representation and visualisation of data in lower dimensional spaces. The methodology is implemented in the cfda R package and is illustrated using a real data set in the clustering framework.
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Submitted on : Tuesday, October 20, 2020 - 8:37:12 PM
Last modification on : Tuesday, December 6, 2022 - 12:42:13 PM
Long-term archiving on: : Thursday, January 21, 2021 - 7:33:36 PM


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  • HAL Id : hal-02973094, version 1



Cristian Preda, Quentin Grimonprez, Vincent Vandewalle. cfda: an R Package for Categorical Functional Data Analysis. 2020. ⟨hal-02973094⟩



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