PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Penalized Partial Least Squares with Applications to B-Splines Transformations and Functional Data
Nicole Krämer, Anne-Laure Boulesteix and Gerhard Tutz
submitted 2007.

Abstract

We propose a novel framework that combines penalization with Partial Least Squares (PLS). Starting with a generalized additive model, we expand each additive component in terms of a generous amount of B-Splines basis functions. In order to prevent overfitting, we estimate the model by applying a penalized version of PLS. This new method can be computed virtually as fast as PLS. Furthermore, we prove a close connection of penalized PLS to preconditioned linear systems. The proposed approach is very general and can be applied to other problems. In particular, we show its benefit for noisy functional data.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3284
Deposited By:Nicole Krämer
Deposited On:07 February 2008