Fast and faster: a comparison of two streamed matrix decomposition algorithms

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Fast and faster: a comparison of two streamed matrix decomposition algorithms
Authors: Radim Řehůřek
Citation: NIPS Workshop on Low-Rank Methods for Large-Scale Machine Learning  : 2010
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Meeting: NIPS Workshop on Low-Rank Methods for Large-Scale Machine Learning
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Link(s): http://arxiv.org/pdf/1102.5597v1
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Fast and faster: a comparison of two streamed matrix decomposition algorithms describes a matrix decomposition algorithm.

To test the developed algorithm the used a matrix with the size 100'000 x 3'199'665 constructed from the English Wikipedia as a term-document matrix, i.e., latent semantic indexing.

The algorithm is associated with the Gensim vector space modeling software implement in the Python programming language.

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