The mRMR variable selection method: a comparative study for functional data

DOI10.1080/00949655.2015.1042378OpenAlexW876004528MaRDI QIDQ5222381

José R. Berrendero, José L. Torrecilla, Antonio Cuevas

Publication date: 1 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1507.03496

zbMATH Keywords

variable selectionfunctional data analysissupervised classificationdistance correlation

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (1)

On the Use of Reproducing Kernel Hilbert Spaces in Functional Classification

Uses Software

• fda (R)
• KernSmooth
• LIBLINEAR
• energy
• ElemStatLearn

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Measuring and testing dependence by correlation of distances
• Detecting Novel Associations in Large Data Sets
• Energy statistics: a class of statistics based on distances
• Feature extraction. Foundations and applications. Papers from NIPS 2003 workshop on feature extraction, Whistler, BC, Canada, December 11--13, 2003. With CD-ROM.
• Brownian distance covariance
• A comparative study of several smoothing methods in density estimation
• On the uniqueness of distance covariance
• Classifier technology and the illusion of progress
• Functional data analysis.
• Nonparametric functional data analysis. Theory and practice.
• Variable selection in functional data classification: a maxima-hunting proposal
• Supervised Classification for a Family of Gaussian Functional Models
• Logistic Regression With Brownian-Like Predictors

This page was built for publication: The mRMR variable selection method: a comparative study for functional data