In multivariate data analysis, Fisher linear discriminant analysis is useful to optimally separate two classes of observations by finding a linear combination of p variables. Functional data analysis deals with the analysis of continuous functions and thus can be seen as a generalisation of multivariate analysis where the dimension of the analysis space p strives to infinity. Several authors propose methods to perform discriminant analysis in this infinite dimensional space. Here, the methodology is introduced to perform discriminant analysis, not on single infinite dimensional functions, but to find a linear combination of p infinite dimensional continuous functions, providing a set of continuous canonical functions which are optimally separated in the canonical space.

在多变量数据分析中，Fisher 线性判别分析有助于通过找到p个变量的线性组合来最佳地分离两类观察值。函数数据分析处理连续函数的分析，因此可以看作是多元分析的泛化，其中分析空间p的维数力求无穷大。几位作者提出了在这个无限维空间中执行判别分析的方法。在这里，引入该方法来执行判别分析，而不是对单个无限维函数，而是找到p的线性组合无限维连续函数，提供一组在规范空间中最佳分离的连续规范函数。