Publication details

Subspace Tracking for Latent Semantic Analysis



Year of publication 2011
Type Article in Proceedings
Conference Proceedings of the 33rd European Conference on Information Retrieval (ECIR)
MU Faculty or unit

Faculty of Informatics

Field Information theory
Keywords scalability svd subspace tracking
Description Modern applications of Latent Semantic Analysis (LSA) must deal with enormous (often practically infinite) data collections, calling for a single-pass matrix decomposition algorithm that operates in constant memory w.r.t. the collection size. This paper introduces a \emph{streamed distributed algorithm for incremental SVD updates}. Apart from the theoretical derivation, we present experiments measuring numerical accuracy and runtime performance of the algorithm over several data collections, one of which is the whole of the English Wikipedia.
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