Feature selection for functional data

JOURNAL OF MULTIVARIATE ANALYSIS

卷:146

Article

Fraiman, Ricardo¹  Gimenez, Yanina^2,3  Svarc, Marcela^2,3 

[1] Univ Republ, Fac Ciencias, Ctr Matemat, Asuncion, Paraguay

[2] Univ San Andres, Dept Matemat, Buenos Aires, DF, Argentina

[3] Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina

关键词: Variable selection;    Classification;    Regression;    Principal components;   

DOI  :  10.1016/j.jmva.2015.09.006

来源: Elsevier

【 摘 要 】

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. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

Free