新疆大学学报（自然科学版中英文）2026，Vol.43Issue(3)：305-323,19.DOI:10.13568/j.cnki.651094.651316.2025.06.24.0001
基于L2,1-范数的函数型数据稀疏典型相关分析
Sparse Canonical Correlation Analysis with L2,1-Norm for Functional Data
摘要
Abstract
Functional canonical correlation analysis is a key method in multivariate statistics for identifying optimal linear correlations between two functional datasets.However,some functions within these datasets may exhibit anomalies such as sudden changes or fluctuations that deviate from the overall trend,resulting to inaccurate results.To address this,we propose an improved method:Sparse functional canonical correlation analysis based on the L2,1-norm.This approach reduces outliers by optimizing the selection of orthogonal basis functions,thereby enhancing the accuracy and reliability of the analysis.Nu-merical experiments show that the L2,1-norm-based method significantly outperforms traditional methods.关键词
函数型典型相关分析/L2,1-范数/函数型数据/异常值/正交基函数Key words
functional canonical correlation analysis/L2,1-norm/functional data/outliers/orthogonal basis function分类
数理科学引用本文复制引用
张泽江,杨志霞,叶俊佑,汪玉兰..基于L2,1-范数的函数型数据稀疏典型相关分析[J].新疆大学学报（自然科学版中英文）,2026,43(3):305-323,19.基金项目
The National Natural Science Foundation of China"Optimization models and algorithms for interpretable lear-ning machines on complex data"(12461058) （12461058）
Xinjiang Key Laboratory of Applied Mathematics(XJDX1401). （XJDX1401）
