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@inproceedings{914342,
author = {Řehůřek, Radim},
booktitle = {NIPS 2010 workshop on Low-rank Methods for Large-scale Machine Learning},
editor = {Michael Mahoney, Ameet Talwalkar, Mehryan Mohri, Arthur Gretton},
keywords = {svd lda lsi},
language = {eng},
title = {Fast and Faster: A Comparison of Two Streamed Matrix Decomposition Algorithms},
url = {http://www.eecs.berkeley.edu/~ameet/low-rank-nips10/},
year = {2010}
}
TY - JOUR
ID - 914342
AU - Řehůřek, Radim
PY - 2010
TI - Fast and Faster: A Comparison of Two Streamed Matrix Decomposition Algorithms
KW - svd lda lsi
UR - http://www.eecs.berkeley.edu/~ameet/low-rank-nips10/
N2 - 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.
ER -
ŘEHŮŘEK, Radim. Fast and Faster: A Comparison of Two Streamed Matrix Decomposition Algorithms. In Michael Mahoney, Ameet Talwalkar, Mehryan Mohri, Arthur Gretton. \textit{NIPS 2010 workshop on Low-rank Methods for Large-scale Machine Learning}. 2010, 7 s.
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