PLS Approach for clusterwise linear regression on functional data - Archive ouverte HAL
Chapitre D'ouvrage Année : 2004

PLS Approach for clusterwise linear regression on functional data

Résumé

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.

Dates et versions

hal-01124925 , version 1 (06-03-2015)

Identifiants

Citer

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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