  This work is motivated by a real work problem: objectivization. It consists
in explaining the subjective drivability using physical criteria coming from
signals measured during experiments. We suggest an approach for the
discriminant variables selection trying to take advantage of the functional
nature of the data. The porblem is ill-posed, since the number of explanatory
variables is hugely greater than the sample size. The strategy proceeds in
three steps: a signal preprocessing including wavelet denoising and
synchronization, dimensionality reduction by compression using a common wavelet
basis, and finally the selection of useful variables using a stepwise strategy
involving successive applications of the CART method.
