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Large-scale matrix factorization using MapReduce
Conference
Sun, Z, Li, T, Rishe, N. (2010). Large-scale matrix factorization using MapReduce .
1242-1248. 10.1109/ICDMW.2010.155
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Sun, Z, Li, T, Rishe, N. (2010). Large-scale matrix factorization using MapReduce .
1242-1248. 10.1109/ICDMW.2010.155
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cited authors
Sun, Z; Li, T; Rishe, N
authors
Rishe, Naphtali
abstract
Due to the popularity of nonnegative matrix factorization and the increasing availability of massive data sets, researchers are facing the problem of factorizing large-scale matrices of dimensions in the orders of millions. Recent research [11] has shown that it is feasible to factorize a million-by-million matrix with billions of nonzero elements on a MapReduce cluster. In this work, we present three different matrix multiplication implementations and scale up three types of nonnegative matrix factorizations on MapReduce. Experiments on both synthetic and real-world datasets show the excellent scalability of our proposed algorithms. © 2010 IEEE.
publication date
December 1, 2010
Identifiers
Digital Object Identifier (DOI)
https://doi.org/10.1109/icdmw.2010.155
International Standard Book Number (ISBN) 13
9780769542577
Additional Document Info
start page
1242
end page
1248