# PCA: When using eigendecompostion instead of single value decompostion [duplicate]

This question already has an answer here:

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?

## marked as duplicate by amoeba, gung♦, kjetil b halvorsen, whuber♦Dec 24 '14 at 1:14

• 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 7 '14 at 17:57