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