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Proper Orthogonal Decomposition (POD), singular value decomposition (SVD), and principal component analysis (PCA) are three eigenvalue methods used to reduce a high-dimensional data set into fewer dimensions while retaining important information. Online articles,e.g., this Wikipedia article, say that these methods are 'related' but does not specify the exact relation.

What is the intuitive relationship between POD, and SVD, PCA? How to decide which one to choose?

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