Feature Selection for Functional Data
arXiv:1502.02123 · doi:10.1016/j.jmva.2015.09.006
Abstract
In this paper we address the problem of feature selection when the data is functional, we study several statistical procedures including classification, regression and principal components. One advantage of the blinding procedure is that it is very flexible since the features are defined by a set of functions, relevant to the problem being studied, proposed by the user. Our method is consistent under a set of quite general assumptions, and produces good results with the real data examples that we analyze.
22 pages, 4 figures
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