Regularized generalized canonical correlation analysis (RGCCA) is a general multiblock data analysis framework that encompasses several important multivariate analysis methods such as principal component analysis, partial least squares regression, and several versions of generalized canonical correlation analysis. In this article, we extend RGCCA to the case where at least one block has a tensor structure. This method is called multiway generalized canonical correlation analysis (MGCCA). Convergence properties of the MGCCA algorithm are studied, and computation of higher-level components are discussed. The usefulness of MGCCA is shown on simulation and on the analysis of a cognitive study in human infants using electroencephalography (EEG).

中文翻译：

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多路广义典型相关分析。

正则化广义典型相关分析 (RGCCA) 是一个通用的多块数据分析框架，包含几种重要的多变量分析方法，如主成分分析、偏最小二乘回归和多个版本的广义典型相关分析。在本文中，我们将 RGCCA 扩展到至少一个块具有张量结构的情况。这种方法称为多路广义典型相关分析（MGCCA）。研究了MGCCA算法的收敛特性，并讨论了高层分量的计算。MGCCA 的有用性体现在模拟和使用脑电图 (EEG) 对人类婴儿的认知研究的分析中。