Robust generalized canonical correlation analysis

被引：4

作者：

Yan, He ^{[1
]}

Cheng, Li ^{[2
]}

Ye, Qiaolin ^{[3
]}

Yu, Dong-Jun ^{[1
]}

Qi, Yong ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Xiaolingwei 200, Nanjing 210094, Peoples R China

[2] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 1H9, Canada

[3] Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Peoples R China

来源：

APPLIED INTELLIGENCE | 2023年 / 53卷 / 18期

基金：

中国国家自然科学基金;

关键词：

Outliers and noise; p-order of Frobenius norm; Robust RGCCA; Squared Frobenius norm; FORMULATION;

D O I：

10.1007/s10489-023-04666-6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Generalized canonical correlation analysis (GCCA) has been widely used for classification and regression problems. The key idea of GCCA is to map the data from different views into a common space with the minimum reconstruction error. However, GCCA employs the squared Frobenius norm as a distance metric to find a latent correlated space without a specific strategy to cope with outliers, thus misguiding the GCCA's training task in real-world applications and leading to suboptimal performance. This inspires us to propose a novel robust formulation for GCCA, namely, GCCA with the p-order (0

引用

页码：21140 / 21155

页数：16

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