Communications of the Korean Institute of Information Scientists and Engineers (정보과학회지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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토픽 모델의 분산 온라인 기계 학습 알고리즘

  • Published : 2014.07.15
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Abstract

Keywords

References

  1. Apache Mahout, http://mahout.apache.org.
  2. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993-1022, Mar. 2003.
  3. K. Canini, L. Shi, and T. Griffiths. Online inference of topics with latent dirichlet allocation. In Proceedings of the International Conference on Artificial Intelligence and Statistics, volume 5, pages 65-72, 2009.
  4. T. Griffiths and M. Steyvers. Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101 (Suppl 1):5228-5235, 2004. https://doi.org/10.1073/pnas.0307752101
  5. J. Hennessy and D. Patterson. Computer architecture: a quantitative approach. Morgan Kaufmann, 2011.
  6. M. Hoffman, D. Blei, and F. Bach. Online learning for latent dirichlet allocation. Advances in Neural Information Processing Systems, 23:856-864, 2010.
  7. J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online learning for matrix factorization and sparse coding. The Journal of Machine Learning Research, 11: 19-60, 2010.
  8. L. Mariote, C. Medeiros, and R. da Torres. Parallelized variational em for latent dirichlet allocation: An experimental evaluation of speed and scalability. In Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on, pages 349-354. IEEE, 2007.
  9. A. K. McCallum. Mallet: A machine learning for language toolkit. http://www.cs.umass.edu/mccallum/mallet, 2002.
  10. D. Newman, A. Asuncion, P. Smyth, and M. Welling. Distributed algorithms for topic models. The Journal of Machine Learning Research, 10: 1801-1828, 2009.
  11. C. Robelt and G. Casella. Monte Carlo Statistical Methods. Springer, 2004.
  12. M. Schatz. Cloudburst: highly sensitive read mapping with mapreduce. Bioinformatics, 25(11): 1363-1369, 2009. https://doi.org/10.1093/bioinformatics/btp236
  13. A. Smola and S. Narayanamurthy. An architecture for parallel topic models. Proceedings of the VLDB Endowment, 3(1-2):703-710, 2010. https://doi.org/10.14778/1920841.1920931
  14. Y. Teh, M. Jordan, M. Beal, and D. Blei. Hierarchical dirichlet processes. Journal of the American Statistical Association, 101(476):1566-1581, 2006. https://doi.org/10.1198/016214506000000302
  15. C. Wang, J. Paisley, and D. Blei. Online variational inference for the hierarchical dirichlet process. In Proc. AISTATS. 2011.
  16. Y. Wang, H. Bai, M. Stanton, W. Chen, and E. Chang. Plda: Parallel latent dirichlet allocation for large-scale applications. Algorithmic Aspects in Information and Management, pages 301-314, 2009.
  17. K. Zhai, J. Boyd-Graber, N. Asadi, and M. L. Alkhouja. Mr. lda: a flexible large scale topic modeling package using variational inference in mapreduce. In Proceedings of the 21 st international conference on World Wide Web, WWW'12, pages 879 - 888, New York, NY, USA, 2012. ACM.