Publication details

Fast and Faster: A Comparison of Two Streamed Matrix Decomposition Algorithms



Year of publication 2010
Type Article in Proceedings
Conference NIPS 2010 workshop on Low-rank Methods for Large-scale Machine Learning
MU Faculty or unit

Faculty of Informatics

Field Information theory
Keywords svd lda lsi
Description With the explosion of the size of digital dataset, the limiting factor for decomposition algorithms is the \emph{number of passes} over the input, as the input is often stored out-of-core or even off-site. Moreover, we're only interested in algorithms that operate in \emph{constant memory} w.r.t. to the input size, so that arbitrarily large input can be processed. In this paper, we present a practical comparison of two such algorithms: a distributed method that operates in a single pass over the input vs. a streamed two-pass stochastic algorithm. The experiments track the effect of distributed computing, oversampling and memory trade-offs on the accuracy and performance of the two algorithms. To ensure meaningful results, we choose the input to be a real dataset, namely the whole of the English Wikipedia, in the application settings of Latent Semantic Analysis.
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