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Volumn 31, Issue 1, 2013, Pages

Regularized latent semantic indexing: A new approach to large-scale topic modeling

Author keywords

Distributed learning; Online learning; Regularization; Sparse methods; Topic modeling

Indexed keywords

DISTRIBUTED LEARNING; ONLINE LEARNING; REGULARIZATION; SPARSE METHODS; TOPIC MODELING;

EID: 84873891666     PISSN: 10468188     EISSN: 15582868     Source Type: Journal    
DOI: 10.1145/2414782.2414787     Document Type: Article
Times cited : (34)

References (56)
  • 3
    • 78649698593 scopus 로고    scopus 로고
    • Asynchronous distributed estimation of topic models for document analysis
    • Asuncion, A., Smyth, P., and Welling, M. 2011. Asynchronous distributed estimation of topic models for document analysis. Stat. Methodol.
    • (2011) Stat. Methodol.
    • Asuncion, A.1    Smyth, P.2    Welling, M.3
  • 4
    • 84861426719 scopus 로고    scopus 로고
    • Latent semantic indexing (lsi) fails for trec collections
    • Atreya, A. and Elkan, C. 2010. Latent semantic indexing (lsi) fails for trec collections. ACM SIGKDD Exp. Newslet. 12.
    • (2010) ACM SIGKDD Exp. Newslet. , pp. 12
    • Atreya, A.1    Elkan, C.2
  • 6
    • 84861420312 scopus 로고    scopus 로고
    • Introduction to probabilistic topic models
    • to appear
    • Blei, D. 2011. Introduction to probabilistic topic models. Commun. ACM. to appear.
    • Commun. ACM , pp. 2011
    • Blei, D.1
  • 10
    • 0032096712 scopus 로고    scopus 로고
    • Optimization problems with perturbations: A guided tour
    • Bonnans, J. F. and Shapiro, A. 1998. Optimization problems with perturbations: A guided tour. SIAM Rev. 40.
    • (1998) SIAM Rev. , vol.40
    • Bonnans, J.F.1    Shapiro, A.2
  • 19
    • 41249089920 scopus 로고    scopus 로고
    • On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing semantic indexing
    • Ding, C., Li, T., and Peng, W. 2008. On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing semantic indexing. Comput. Stat. Data Anal. 52.
    • (2008) Comput. Stat. Data Anal. , vol.52
    • Ding, C.1    Li, T.2    Peng, W.3
  • 20
    • 17644368222 scopus 로고    scopus 로고
    • A probabilistic model for latent semantic indexing
    • Ding, C. H. Q. 2005. A probabilistic model for latent semantic indexing. J. Amer. Soc. Inf. Sci. Technol. 56.
    • (2005) J. Amer. Soc. Inf. Sci. Technol. , vol.56
    • Ding, C.H.Q.1
  • 23
    • 0032361278 scopus 로고    scopus 로고
    • Penalized regressions: The bridge versus the lasso
    • Fu, W. J. 1998. Penalized regressions: The bridge versus the lasso. J. Comput. Graphi. Stat. 7.
    • (1998) J. Comput. Graphi. Stat. , vol.7
    • Fu, W.J.1
  • 27
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects with nonnegative matrix factorization
    • Lee, D. D. and Seung, H. S. 1999. Learning the parts of objects with nonnegative matrix factorization. Nature 401.
    • (1999) Nature , vol.401
    • Lee, D.D.1    Seung, H.S.2
  • 32
    • 79955694310 scopus 로고    scopus 로고
    • Plda+: Parallel latent dirichlet allocation with data placement and pipeline processing
    • Liu, Z., Zhang, Y., and Chang, E. Y. 2011. Plda+: Parallel latent dirichlet allocation with data placement and pipeline processing. ACM Trans. Intell. Syst. Technol. 2.
    • (2011) ACM Trans. Intell. Syst. Technol. , vol.2
    • Liu, Z.1    Zhang, Y.2    Chang, E.Y.3
  • 33
    • 79953230442 scopus 로고    scopus 로고
    • Investigating task performance of probabilistic topic models: An empirical study of plsa and lda
    • Lu, Y., Mei, Q., and Zhai, C. 2011. Investigating task performance of probabilistic topic models: An empirical study of plsa and lda. Inf. Retrieval 14.
    • (2011) Inf. Retrieval , vol.14
    • Lu, Y.1    Mei, Q.2    Zhai, C.3
  • 38
    • 0002788893 scopus 로고    scopus 로고
    • A view of the em algorithm that justifies incremental, sparse, and other variants
    • Neal, R. M. and Hinton, G. E. 1998. A view of the em algorithm that justifies incremental, sparse, and other variants. Learn. Graph. Models 89.
    • (1998) Learn. Graph. Models , vol.89
    • Neal, R.M.1    Hinton, G.E.2
  • 40
    • 0030779611 scopus 로고    scopus 로고
    • Sparse coding with an overcomplete basis set: A strategy employed by v1
    • Olshausen, B. A. and Fieldt, D. J. 1997. Sparse coding with an overcomplete basis set: A strategy employed by v1. Vision Res. 37.
    • (1997) Vision Res. , vol.37
    • Olshausen, B.A.1    Fieldt, D.J.2
  • 41
    • 0034215549 scopus 로고    scopus 로고
    • A new approach to variable selection in least squares problems
    • Osborne, M., Presnell, B., and Turlach, B. 2000. A new approach to variable selection in least squares problems. IMA J. Numer. Anal.
    • (2000) IMA J. Numer. Anal.
    • Osborne, M.1    Presnell, B.2    Turlach, B.3
  • 43
    • 84900231120 scopus 로고    scopus 로고
    • Double sparsity: Learning sparse dictionaries for sparse signal approximation
    • Rubinstein, R., Zibulevsky, M., and Elad, M. 2008. Double sparsity: Learning sparse dictionaries for sparse signal approximation. IEEE Trans. Signal Process.
    • (2008) IEEE Trans. Signal Process.
    • Rubinstein, R.1    Zibulevsky, M.2    Elad, M.3
  • 44
    • 0016572913 scopus 로고
    • A vector space model for automatic indexing
    • Salton, G., Wong, A., and Yang, C. S. 1975. A vector space model for automatic indexing. Commun. ACM 18.
    • (1975) Commun. ACM , vol.18
    • Salton, G.1    Wong, A.2    Yang, C.S.3
  • 48
    • 14744288044 scopus 로고    scopus 로고
    • Optimization of collective communication operations in mpich
    • Thakur, R. and Rabenseifner, R. 2005. Optimization of collective communication operations in mpich. Int. J. High Perform. Comput. 19.
    • (2005) Int. J. High Perform. Comput. , vol.19
    • Thakur, R.1    Rabenseifner, R.2
  • 49
    • 84873905099 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso. J. Royal stat. Soc. Wang, c. And blei, d. M. 2009. Decoupling sparsity and smoothness in the discrete hierachical dirichlet process
    • MIT Press, Cambridge, MA
    • Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. J. Royal Stat. Soc. Wang, C. and Blei, D. M. 2009. Decoupling sparsity and smoothness in the discrete hierachical dirichlet process. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA.
    • (1996) Advances in Neural Information Processing Systems.
    • Tibshirani, R.1
  • 54
    • 79955691941 scopus 로고    scopus 로고
    • Parallel inference for latent dirichlet allocation on graphics processing units
    • MIT Press, Cambridge, MA
    • Yan, F., Xu, N., and Qi, Y. A. 2009. Parallel inference for latent dirichlet allocation on graphics processing units. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA.
    • (2009) Advances in Neural Information Processing Systems.
    • Yan, F.1    Xu, N.2    Qi, Y.A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.