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Volumn 2430, Issue , 2002, Pages 23-34

Variational extensions to EM and multinomial PCA

Author keywords

[No Author keywords available]

Indexed keywords

FACTORIZATION; IMAGE SEGMENTATION; MACHINE LEARNING; MAXIMUM PRINCIPLE; SEMANTICS; STATISTICS;

EID: 84945258136     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36755-1_3     Document Type: Conference Paper
Times cited : (72)

References (13)
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    • Mixtures of probabilistic principal component analysers
    • Tipping, M., Bishop, C.: Mixtures of probabilistic principal component analysers. Neural Computation 11 (1999) 443-482
    • (1999) Neural Computation , vol.11 , pp. 443-482
    • Tipping, M.1    Bishop, C.2
  • 3
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • Lee, D., Seung, H.: Learning the parts of objects by non-negative matrix factorization. Nature 401 (1999) 788-791
    • (1999) Nature , vol.401 , pp. 788-791
    • Lee, D.1    Seung, H.2
  • 4
    • 84898995847 scopus 로고    scopus 로고
    • Latent Dirichlet allocation
    • Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. In: NIPS 14. (2002) to appear.
    • (2002) NIPS 14
    • Blei, D.1    Ng, A.2    Jordan, M.3
  • 5
    • 84899014910 scopus 로고    scopus 로고
    • A generalization of principal component analysis to the exponential family
    • Collins, M., Dasgupta, S., Schapire, R.: A generalization of principal component analysis to the exponential family. In: NIPS 13. (2001)
    • (2001) NIPS 13
    • Collins, M.1    Dasgupta, S.2    Schapire, R.3
  • 6
    • 33144483519 scopus 로고    scopus 로고
    • Learning curved multinomial subfamilies for natural language processing and information retrieval
    • Hall, K., Hofmann, T.: Learning curved multinomial subfamilies for natural language processing and information retrieval. In: ICML 2000. (2000)
    • (2000) ICML 2000
    • Hall, K.1    Hofmann, T.2
  • 8
    • 0000675167 scopus 로고    scopus 로고
    • Structure learning in conditional probability models via an entropicprior and parameter extinction
    • Brand, M.: Structure learning in conditional probability models via an entropicprior and parameter extinction. Neural Computation 11 (1999) 1155-1182
    • (1999) Neural Computation , vol.11 , pp. 1155-1182
    • Brand, M.1
  • 9
    • 84898929664 scopus 로고    scopus 로고
    • EM algorithms for PCA and SPCA
    • Roweis, S.: EM algorithms for PCA and SPCA. In: NIPS 10. (1998)
    • (1998) NIPS 10
    • Roweis, S.1
  • 10
    • 0001330172 scopus 로고    scopus 로고
    • Propagation algorithms for variational Bayesian learning
    • Ghahramani, Z., Beal, M.: Propagation algorithms for variational Bayesian learning. In: NIPS. (2000) 507-513
    • (2000) NIPS , pp. 507-513
    • Ghahramani, Z.1    Beal, M.2
  • 12
    • 84945315747 scopus 로고    scopus 로고
    • Estimating a Dirichlet distribution
    • Minka, T.: Estimating a Dirichlet distribution. Course notes (2000)
    • (2000) Course notes
    • Minka, T.1
  • 13
    • 0033225865 scopus 로고    scopus 로고
    • An introduction to variational methods for graphical models
    • Jordan, M., Ghahramani, Z., Jaakkola, T., Saul, L.: An introduction to variational methods for graphical models. Machine Learning 37 (1999) 183-233
    • (1999) Machine Learning , vol.37 , pp. 183-233
    • Jordan, M.1    Ghahramani, Z.2    Jaakkola, T.3    Saul, L.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.