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Given a dataset PCA can be performed via 3 ways:

  1. Eigenvalue decomposition
  2. Singular value decomposition
  3. Non-linear iterative partial least-squares algorithm

Can anyone shed light on comparative study of the 3 ways, what is pros and cons of each way? when to prefer a particular method?

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  • $\begingroup$ There actually has been answered questions like this one. Though they don't necessarily refer to a "comparative study". Here is one stats.stackexchange.com/q/79043/3277. Search other yourself, please. $\endgroup$
    – ttnphns
    Commented Nov 5, 2021 at 8:36
  • $\begingroup$ @ttnphns Correct. But, I don't see any question on Non-linear iterative partial least-squares algorithm for PCA and its comparison with say Eigen decomposition. Please mark this as duplicate with link to any such question which discusses Non-linear iterative partial least-squares algorithm vs eigen decomposition. Thanks. $\endgroup$
    – user13744439
    Commented Nov 5, 2021 at 13:38

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