Looking for some software to deal with 50kx50k sparse matrix applying non-negative matrix factorization. Do you know any?
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$\begingroup$ Third hit in google leads me here: cs.helsinki.fi/u/phoyer/software.html $\endgroup$– Terry LoringSep 2 '13 at 16:11
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2$\begingroup$ It would help if you were a bit more verbose on what exactly you mean with "non-negative", and what kinds of factorizations you are looking for. What do you happen to know about your matrix? $\endgroup$– Wolfgang BangerthSep 3 '13 at 3:02
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1$\begingroup$ non-negative matrix factorization is a thing $\endgroup$– k20Sep 3 '13 at 21:32
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$\begingroup$ @k20, several terms are "a thing", but it would not hurt to be descriptive. $\endgroup$– nicoguaro ♦Mar 13 '18 at 13:19
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The following paper uses MapReduce for distributed computation of non-negative matrix factorization:
- J. Yin, L. Gao, Zh. Zhang, "Scalable Nonnegative Matrix Factorization with Block-wise Updates," in Proc. Eu. Conf. Machine Learning Knowledge Discovery, Nancy, France, Sep. 2014, pp.337-352.
This paper describes an implementation and parallelization of the non-negative factorization and claims the results for matrices exceeding your specifications.
Regarding software libraries:
- python library NIMFA supports both dense and sparse input (associated paper)
- R-package NMF (no info on sparse that I was able to find quickly)
- NMF Matrix toolbox (v 1.4 added support for sparse input)
- high-performance library SmallK, supports both dense and sparse
answered Mar 13 '18 at 13:02