Efficient kernel canonical correlation analysis using Nyström approximation

被引：0
作者：
Fang, Qin [1 ]
Shi, Lei [2 ,3 ,4 ]
Xu, Min [5 ]
Zhou, Ding-Xuan [6 ]
机构：
[1] Dalian Univ, Informat & Engn Coll, Dalian 116622, Peoples R China
[2] Fudan Univ, Sch Math Sci, Shanghai 200433, Peoples R China
[3] Fudan Univ, Shanghai Key Lab Contemporary Appl Math, Shanghai 200433, Peoples R China
[4] Shanghai Artificial Intelligence Lab, 701 Yunjin Rd, Shanghai 200232, Peoples R China
[5] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China
[6] Univ Sydney, Sch Math & Stat, Sydney, NSW 2006, Australia
来源：
INVERSE PROBLEMS | 2024年 / 40卷 / 04期
基金：
中国国家自然科学基金;
关键词：
kernel canonical correlation analysis; Nystrom approximation; cross-covariance operator; covariance; NYSTROM METHOD; 2; SETS; ALGORITHMS;
D O I：
10.1088/1361-6420/ad2900
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The main contribution of this paper is the derivation of non-asymptotic convergence rates for Nystrom kernel canonical correlation analysis (CCA) in a setting of statistical learning. Our theoretical results reveal that, under certain conditions, Nystrom kernel CCA can achieve a convergence rate comparable to that of the standard kernel CCA, while offering significant computational savings. This finding has important implications for the practical application of kernel CCA, particularly in scenarios where computational efficiency is crucial. Numerical experiments are provided to demonstrate the effectiveness of Nystrom kernel CCA.
引用
收藏
页数：26
相关论文
共 50 条
  • [31] Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data
    Kosuke Yoshida
    Junichiro Yoshimoto
    Kenji Doya
    BMC Bioinformatics, 18
  • [32] Specific Emitter Identification through Multi-Domain Mixed Kernel Canonical Correlation Analysis
    Chen, Jian
    Li, Shengyong
    Qi, Jianchi
    Li, Hongke
    ELECTRONICS, 2024, 13 (07)
  • [33] Key-performance-indicator-related state monitoring based on kernel canonical correlation analysis
    Chen, Qing
    Wang, Youqing
    CONTROL ENGINEERING PRACTICE, 2021, 107
  • [34] Data-distribution-informed Nyström approximation for structured data using vector quantization-based landmark determination
    Muench, Maximilian
    Bohnsack, Katrin Sophie
    Schleif, Frank-Michael
    Villmann, Thomas
    NEUROCOMPUTING, 2024, 596
  • [35] Convergence analysis of regularised Nyström method for functional linear regression
    Gupta, Naveen
    Sivananthan, S.
    INVERSE PROBLEMS, 2025, 41 (04)
  • [36] Tensor canonical correlation analysis
    Min, Eun Jeong
    Chi, Eric C.
    Zhou, Hua
    STAT, 2019, 8 (01):
  • [37] A Survey on Canonical Correlation Analysis
    Yang, Xinghao
    Liu, Weifeng
    Liu, Wei
    Tao, Dacheng
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2021, 33 (06) : 2349 - 2368
  • [38] Incremental Canonical Correlation Analysis
    Zhao, Hongmin
    Sun, Dongting
    Luo, Zhigang
    APPLIED SCIENCES-BASEL, 2020, 10 (21): : 1 - 13
  • [39] Fair Canonical Correlation Analysis
    Zhoup, Zhuoping
    Tarzanagh, Davoud Ataee
    Hou, Bojian
    Tong, Boning
    Xu, Jia
    Feng, Yanbo
    Long, Qi
    Shen, Li
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [40] Multiset Canonical Correlation Analysis Using for Blind Source Separation
    Yu, Huagang
    Huang, Gaoming
    Gao, Jun
    MECHANICAL ENGINEERING AND INTELLIGENT SYSTEMS, PTS 1 AND 2, 2012, 195-196 : 104 - 108
12345