Multilayer Perceptron with Functional Inputs: an Inverse Regression Approach

Details Multilayer Perceptron with Functional Inputs: an Inverse Regression Approach Multilayer Perceptron with Functional Inputs: an Inverse Regression Approach

2007-05-02

arxiv.org/abs/0705.0211v1

Scandinavian Journal of Statistics 33, 4 (12/2006) 807-823

Mathematics

Statistics Theory

17 pages

Scientific paper

10.1111/j.1467-9469.2006.00496.x

Functional data analysis is a growing research field as more and more practical applications involve functional data. In this paper, we focus on the problem of regression and classification with functional predictors: the model suggested combines an efficient dimension reduction procedure [functional sliced inverse regression, first introduced by Ferr\'e & Yao (Statistics, 37, 2003, 475)], for which we give a regularized version, with the accuracy of a neural network. Some consistency results are given and the method is successfully confronted to real-life data.

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