# How to Compute the EOFs by SVD?

• I
Considering I have a matrix ##\mathbf{A}## which has a size of ##M \times N##, how can I compute the Empirical Orthogonal Functions (EOFs) by Singular Value Decomposition (SVD)?

According to SVD, the matrix ##\mathbf{A}## is

##\mathbf{A} = \mathbf{U} \mathbf{\Sigma} \mathbf{V}^{T}##

where a superscript of ##T## denotes a transpose. Now, which are the EOFs in this equation, are they the rows of ##\mathbf{V}^{T}## or its columns (the rows of ##\mathbf{V}##)?

Thanks for the post! This is an automated courtesy bump. Sorry you aren't generating responses at the moment. Do you have any further information, come to any new conclusions or is it possible to reword the post?

Hi there. The EOFs are the columns of V^T.

This site helps me to figure this out https://pmc.ucsc.edu/~dmk/notes/EOFs/EOFs.html. Goodnight from your atemporal interested in orthostatics guy.