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AAIM
2009
Springer

PLDA: Parallel Latent Dirichlet Allocation for Large-Scale Applications

9 years 6 months ago
PLDA: Parallel Latent Dirichlet Allocation for Large-Scale Applications
Abstract. This paper presents PLDA, our parallel implementation of Latent Dirichlet Allocation on MPI and MapReduce. PLDA smooths out storage and computation bottlenecks and provides fault recovery for lengthy distributed computations. We show that PLDA can be applied to large, real-world applications and achieves good scalability. We have released MPI-PLDA to open source at http://code.google.com/p/plda under the Apache License.
Yi Wang, Hongjie Bai, Matt Stanton, Wen-Yen Chen,
Added 23 Jul 2010
Updated 23 Jul 2010
Type Conference
Year 2009
Where AAIM
Authors Yi Wang, Hongjie Bai, Matt Stanton, Wen-Yen Chen, Edward Y. Chang
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