0

If you want to perform a PCA, I guess that using SVD will always work. Eigendecomposition on the covariance matrix only works when your data is not high dimensional(so n > p). But I'm wonder if there are circumstances in practice where you would prefer eigendecompostion over SVD?

statastic
  • 261
  • 1
  • 10
  • What is a "use case"? – Nick Cox May 07 '14 at 17:54
  • 3
    Normally "$n$" refers to the number of cases and "$p$" to the number of variables--the "dimension" of the data. Thus the high-dimensional case would ordinarily be understood as $p\gg n,$ rather than $n\gt p.$ – whuber May 07 '14 at 17:57
  • @NickCox I am not sure since I do not know when you would prefer to use it. Maybe it's computationally more efficient? – statastic May 08 '14 at 04:28
  • @whuber It's what I meant. Maybe it was not written clear enough. Sorry – statastic May 08 '14 at 04:29
  • 1
    I don't know what you mean by the words "use case". I guess "usecase" is a typo for "use case", but the doubt remains. Perhaps you mean: are there circumstances in practice where ...? – Nick Cox May 08 '14 at 08:18
  • exactly what I meant. – statastic May 09 '14 at 14:34
  • 1
    http://stats.stackexchange.com/q/79043/3277 is what you might want to read. The short answer: both work equivalently (no, you are not right saying that eigen-decomposition is for n>p only), however eigen is generally faster (if n>p) and svd is just slightly more precise. – ttnphns May 14 '14 at 19:01

0 Answers0