Wasserstein filter for variable screening in binary classification in the reproducing kernel Hilbert space
type: Item
instance of: scholarly article
title: Wasserstein filter for variable screening in binary classification in the reproducing kernel Hilbert space
DOI: 10.1080/10485252.2023.2235430
author: Sanghun Jeong
author: Choongrak Kim
author: Hojin Yang
published in: Journal of Nonparametric Statistics
publication date: 2024-09-26T00:00:00Z
Mathematics Subject Classification ID: 62G05
Mathematics Subject Classification ID: 62G99
zbMATH DE Number: 7919210
MaRDI profile type: Publication
cites work: Q5488485
cites work: Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
cites work: The Frechet distance between multivariate normal distributions
cites work: High-dimensional classification using features annealed independence rules
cites work: Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
cites work: Sure Independence Screening for Ultrahigh Dimensional Feature Space
cites work: A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
cites work: Sure independence screening in generalized linear models with NP-dimensionality
cites work: Q3174083
cites work: Kernel dimension reduction in regression
cites work: On a Formula for the L2 Wasserstein Metric between Measures on Euclidean and Hilbert Spaces
cites work: Q5405143
cites work: Algorithmic Learning Theory
cites work: The Elements of Statistical Learning
cites work: Kernel methods in machine learning
cites work: Q5838558
cites work: The Kolmogorov filter for variable screening in high-dimensional binary classification
cites work: Inference for Empirical Wasserstein Distances on Finite Spaces
cites work: Q5405165
cites work: Random distributional response model based on spline method
cites work: Quantile Function on Scalar Regression Analysis for Distributional Data
cites work: MILFM: Multiple index latent factor model based on high-dimensional features
cites work: Recursive partitioning and applications
description: scientific article; zbMATH DE number 7919210