Karhunen-Loève Theorem

In the field of functional data analysis, the Karhunen-Loève theorem serves as the canonical representation for sample paths of continuous random processes. Each observed curve is decomposed into its mean function plus a weighted sum of eigenfunctions, with the weights being random variables that capture the individual deviation from the population mean. This representation makes functional data analysis a direct extension of multivariate statistics into infinite dimensions.