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.
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收藏
页数：26
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