  Dimensionality reduction is one of the key issues in the design of effective
machine learning methods for automatic induction. In this work, we introduce
recursive maxima hunting (RMH) for variable selection in classification
problems with functional data. In this context, variable selection techniques
are especially attractive because they reduce the dimensionality, facilitate
the interpretation and can improve the accuracy of the predictive models. The
method, which is a recursive extension of maxima hunting (MH), performs
variable selection by identifying the maxima of a relevance function, which
measures the strength of the correlation of the predictor functional variable
with the class label. At each stage, the information associated with the
selected variable is removed by subtracting the conditional expectation of the
process. The results of an extensive empirical evaluation are used to
illustrate that, in the problems investigated, RMH has comparable or higher
predictive accuracy than the standard dimensionality reduction techniques, such
as PCA and PLS, and state-of-the-art feature selection methods for functional
data, such as maxima hunting.
