We present a novel algorithm that creates
document vectors with reduced dimensionality.
This work was motivated by
an application characterizing relationships among documents
in a collection.
Our algorithm yielded inter-document similarities with
an average precision up to 17.8%
higher than that of singular value decomposition (SVD)
used for Latent Semantic Indexing.
The best performance was
achieved with dimensional reduction rates that were
43% higher than SVD on average.
Our algorithm creates basis vectors for a reduced space by
iteratively ``scaling'' vectors and computing eigenvectors.
it breaks the symmetry of documents and terms
to capture information more evenly across documents.
We also discuss correlation with a probabilistic model and evaluate
a method for selecting the
dimensionality using log-likelihood