Riemannian-based neural network method for solving canonical correlation analysis

被引：0
|
作者：
Zhuo-Cheng Xie [1 ]
Ming Wang [1 ]
Yu-Hang Wang [1 ]
Huan Ren [1 ]
机构：
[1] School of Mathematical Sciences, Jiangxi Science and Technology Normal University, Nanchang
来源：
Computational and Applied Mathematics | 2025年 / 44卷 / 5期
基金：
中国国家自然科学基金;
关键词：
Canonical correlation analysis; Generalized Stiefel manifold; Neural network method; Riemannian gradient;
D O I：
10.1007/s40314-025-03197-9
中图分类号：
学科分类号：
摘要：
Canonical correlation analysis is a classic statistical technique. In this paper, we develop a neural network method based on the Riemannian gradient for solving canonical correlation analysis problems. For the theoretical analysis, the geometric dynamics properties of this method are investigated. Numerical experiments indicate the feasibility and effectiveness of the proposed neural network method. © The Author(s) under exclusive licence to Sociedade Brasileira de Matemática Aplicada e Computacional 2025.
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