Feature selection for functional data

Ricardo Fraiman, Yanina Gimenez and Marcela Svarc

Journal of Multivariate Analysis, 2016, vol. 146, issue C, 191-208

Abstract: We herein introduce a general procedure to capture the relevant information from a functional data set in relation to a statistical method used to analyze the data, such as, classification, regression or principal components. The aim is to identify a small subset of functions that can “better explain” the model, highlighting its most important features. We obtain consistency results for our proposals. The computational aspects are analyzed, a heuristic stochastic algorithm is introduced and real data sets are studied.

Keywords: Variable selection; Classification; Regression; Principal components (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.jmva.2015.09.006

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