Developing the maximum likelihood estimation for component analysis: A state space approach

传统上，科学家在探索性数据分析中使用成分分析(CA)作为描述性工具。在数学上，分量被定义为观察变量的线性组合。CA的原型是主成分分析。CA的其他基本方法包括典型相关分析和冗余分析。在过去的几十年里，这些基本组件模型的各种推广被开发出来，包括同时组件分析、三向组件分析、多集典型相关分析和扩展冗余分析，仅举几例。

Traditionally, scientists have used component analysis (CA) as descriptive tools in exploratory data analysis. Mathematically, a component is defined as a linear combination of the observed variables. The prototype of CA is principal component analysis. Other basic methods of CA include canonical correlation analysis and redundancy analysis. Over the past several decades, various generalizations of these basic component models were developed, including simultaneous component analysis, three-way component analysis, multiple-set canonical correlation analysis, and extended redundancy analysis, just to name a few.