Karhunen–Loeve Decomposition (K-L展开)

WIKI概述

In the theory of stochastic processes, the Karhunen–Loève theorem (named after Kari Karhunen and Michel Loève), also known as the Kosambi–Karhunen–Loève theorem[1][2] is a representation of a stochastic process as an infinite linear combination of orthogonal functions, analogous to a Fourier series representation of a function on a bounded interval. The transformation is also known as Hotelling transform and eigenvector transform, and is closely related to the principal component analysis (PCA) technique widely used in image processing and in data analysis in many fields.[3]

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截图自 Li, Jie, and Jianbing Chen. Stochastic dynamics of structures. John Wiley & Sons, 2009.

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