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Algorithmic Aspects in Information and Management
Volume 5564 of the series Lecture Notes in Computer Science pp 301-314
PLDA: Parallel Latent Dirichlet Allocation for Large-Scale Applications
- Yi WangAffiliated withGoogle Beijing Research
- , Hongjie BaiAffiliated withGoogle Beijing Research
- , Matt StantonAffiliated withComputer Science, CMU
- , Wen-Yen ChenAffiliated withGoogle Beijing Research
- , Edward Y. ChangAffiliated withGoogle Beijing Research
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.
- Title
- PLDA: Parallel Latent Dirichlet Allocation for Large-Scale Applications
- Book Title
- Algorithmic Aspects in Information and Management
- Book Subtitle
- 5th International Conference, AAIM 2009, San Francisco, CA, USA, June 15-17, 2009. Proceedings
- Pages
- pp 301-314
- Copyright
- 2009
- DOI
- 10.1007/978-3-642-02158-9_26
- Print ISBN
- 978-3-642-02157-2
- Online ISBN
- 978-3-642-02158-9
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- 5564
- Series ISSN
- 0302-9743
- Publisher
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- Additional Links
- Topics
- Industry Sectors
- eBook Packages
- Editors
-
- Andrew V. Goldberg (16)
- Yunhong Zhou (17)
- Editor Affiliations
-
- 16. Microsoft Research – Silicon Valley
- 17. Rocket Fuel Inc.
- Authors
-
- Yi Wang (18)
- Hongjie Bai (18)
- Matt Stanton (19)
- Wen-Yen Chen (18)
- Edward Y. Chang (18)
- Author Affiliations
-
- 18. Google Beijing Research, Beijing, 100084, China
- 19. Computer Science, CMU, USA
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