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Mathematics
Set Theory, Logic, Probability, Statistics
Differences between the PCA function and Karhunen-Loève expansion
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[QUOTE="marcusl, post: 6216145, member: 31593"] I am not an expert in this topic, but I will try to answer since no one else has jumped in. The K-L expansion is not equivalent to an eigenvector decomposition but rather is a more general decomposition onto an orthogonal basis. EV decomp. and Fourier analysis are two specific examples of K-L. (This is presumably why Matlab doesn't have a KL function, because it takes very different forms depending on the problem being solved.) Given that, I don't really understand your question. I also am missing why you are mixing up the terms. Matlab documentation seems very clear in defining what each variable means. Finally, I assume that you are not taking EV's of a random variable but are decomposing its covariance matrix, instead? [/QUOTE]
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Differences between the PCA function and Karhunen-Loève expansion
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