I am trying to use mahout's stochastic svd algorithm on a small dataset to
compare it with the regular svd algorithm (DistributedLanczos).I built the
covariance matrix for the dataset and I feed it to mahout svd which returns a
cleaneigenvectors file with the eigenvectors of this covariance matrix.How can
I compute the equivalent of cleaneigenvectors with mahout ssvd?mahout ssvd
produces 3 folders with the 3 matrices of the svd decomposition (U, V and
Sigma). Is the file in V folder equivalent to cleaneigenvectors? Thanks Deb
