Abstract For the analysis of multiblock data, a unified approach of several strategies such as Generalized Canonical Correlation Analysis (GCCA), Multiblock Principal Components Analysis (MB-PCA), Hierarchical Principal Components Analysis (H-PCA) and ComDim is outlined. These methods are based on the determination of global and block components. The unified approach postulates, on the one hand, two link functions that relate the block components to their associated global components and, on the other hand, two summing up expressions to compute the global components from their associated block components. Not only several well-known methods are retrieved but we also introduce a variant of GCCA. More generally, we hint to other possibilities of extensions thus emphasizing the fact that the unified approach, besides being simple, is versatile. We also show how this approach of analysis although basically unsupervised could be adapted to yield a supervised method to be used for a prediction purpose. Illustrations on the basis of simulated and real case studies are discussed.

中文翻译：

---

无监督多块数据分析：统一的方法和扩展

摘要 针对多块数据的分析，提出了广义典型相关分析（GCCA）、多块主成分分析（MB-PCA）、分层主成分分析（H-PCA）和ComDim等多种策略的统一方法。这些方法基于全局和块组件的确定。统一方法一方面假定两个链接函数将块组件与其相关联的全局组件相关联，另一方面假定两个求和表达式以从其相关联的块组件计算全局组件。不仅检索了几种众所周知的方法，而且我们还介绍了 GCCA 的变体。更一般地说，我们暗示了扩展的其他可能性，从而强调了这样一个事实，即统一方法除了简单之外，还具有通用性。我们还展示了这种分析方法（尽管基本上是无监督的）如何适用于产生用于预测目的的监督方法。讨论了基于模拟和真实案例研究的插图。