doesn't have such API. Is it possible that I
write
eigenvalue decomposition from scratch? What should I do? Thanks a
lot!
Miles Yao
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The SVD does not in general give you eigenvalues of its input.
Are you just trying to access the U and V matrices? they are also
returned in the API. But they are not the eigenvectors of M, as you
note.
I don't think MLlib has anything to help with the general eigenvector problem.
Maybe you can
@Miles, eigen-decomposition with asymmetric matrix doesn't always give
real-value solutions, and it doesn't have the nice properties that
symmetric matrix holds. Usually you want to symmetrize your asymmetric
matrix in some way, e.g. see
Generally adjacency matrix is undirected(symmetric) on social network, so
you can get eigenvectors from SVD computed result.
A = UDV^t
The first column of U is the biggest eigenvector corresponding to the first
value of D.
xj @ Tokyo
On Sat, Aug 9, 2014 at 4:08 AM, Li Pu
-can-I-implement-eigenvalue-decomposition-in-Spark-tp11646.html
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have such API. Is it possible that I write
eigenvalue decomposition from scratch? What should I do? Thanks a lot!
Miles Yao
View this message in context: How can I implement eigenvalue
decomposition
in Spark?
Sent from the Apache Spark User List
? What should I do? Thanks a lot!
Miles Yao
View this message in context: How can I implement eigenvalue
decomposition
in Spark?
Sent from the Apache Spark User List mailing list archive at Nabble.com
decomposition from scratch? What should I do? Thanks a lot!
Miles Yao
View this message in context: How can I implement eigenvalue
decomposition
in Spark?
Sent from the Apache Spark User List mailing list archive at
Nabble.com
View this message in context: How can I implement eigenvalue
decomposition
in Spark?
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