2004 | OriginalPaper | Buchkapitel
PLS Approach for Clusterwise Linear Regression on Functional Data
verfasst von : Cristian Preda, Gilbert Saporta
Erschienen in: Classification, Clustering, and Data Mining Applications
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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The Partial Least Squares (PLS) approach is used for the clusterwise linear regression algorithm when the set of predictor variables forms an 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 to stock-exchange data.