Hi,
I don't think anybody answered this question...
fintis wrote
How do I match the principal components to the actual features since there
is some sorting?
Would anybody be able to shed a little light on it since I too am struggling
with this?
Many thanks!!
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computePrincipalComponents returns a local matrix X, whose columns are
the principal components (ordered), while those column vectors are in
the same feature space as the input feature vectors. -Xiangrui
On Thu, Oct 16, 2014 at 2:39 AM, al123 ant.lay...@hotmail.co.uk wrote:
Hi,
I don't think
sowen wrote
it seems that the singular values from the SVD aren't returned, so I don't
know that you can access this directly
Its not clear to me why these aren't returned? The S matrix would be useful
to determine a reasonable value for K.
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In its current implementation, the principal components are computed in
MLlib in two steps:
1) In a distributed fashion, compute the covariance matrix - the result is
a local matrix.
2) On this local matrix, compute the SVD.
The sorting comes from the SVD. If you want to get the eigenvalues out,
To clarify, you are looking for eigenvectors of what, the covariance
matrix? So for example you are looking for the sqrt of the eigenvalues when
you talk about stdev of components?
Looking at
Hi,
Can anyone please shed more light on the PCA implementation in spark? The
documentation is a bit leaving as I am not sure I understand the output.
According to the docs, the output is a local matrix with the columns as
principal components and columns sorted in descending order of
