Variable selection in functional data classification: a maxima-hunting proposal

Variable selection is considered in the setting of supervised binary classification with functional data $\{X(t),\ t\in[0,1]\}$. By "variable selection" we mean any dimension-reduction method which leads to replace the whole trajectory $\{X(t),\ t\in[0,1]\}$, with a low-dimensional vector $(X(t_1),\ldots,X(t_k))$ still keeping a similar classification error. Our proposal for variable selection is based on the idea of selecting the local maxima $(t_1,\ldots,t_k)$ of the function ${\mathcal V}_X^2(t)={\mathcal V}^2(X(t),Y)$, where ${\mathcal V}$ denotes the "distance covariance" association measure for random variables due to Székely, Rizzo and Bakirov (2007). This method provides a simple natural way to deal with the relevance vs. redundancy trade-off which typically appears in variable selection. This paper includes (a) Some theoretical motivation: a result of consistent estimation on the maxima of ${\mathcal V}_X^2$ is shown. We also show different theoretical models for the underlying process $X(t)$ under which the relevant information in concentrated in the maxima of ${\mathcal V}_X^2$. (b) An extensive empirical study, including about 400 simulated models and real data examples, aimed at comparing our variable selection method with other standard proposals for dimension reduction.

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