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PLS Approach for clusterwise linear regression on functional data

Cristian Preda 1 Gilbert Saporta 2
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
2 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : Partial Least Squares approach is used for the clusterwise linear regression algorithm when the set of predictor variables forms a L2 continuous stochastic process.The number of clusters is treated as unknown and the convergence of the clusterwise algorithm is discussed.The approach is compared with other methods via an application on stock-exchange data.
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Submitted on : Friday, March 6, 2015 - 10:52:53 AM
Last modification on : Friday, November 27, 2020 - 2:18:02 PM

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Cristian Preda, Gilbert Saporta. PLS Approach for clusterwise linear regression on functional data. D.Banks. Classification, Clustering, and Data Mining Applications, Springer; Springer Berlin Heidelberg, pp.167-176, 2004, Classification, Clustering and Data Mining Applications, ⟨10.1007/978-3-642-17103-1_17⟩. ⟨hal-01124925⟩

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