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Volumn 34, Issue 11, 2003, Pages 55-66

Variational Bayes Method for Mixture of Principal Component Analyzers

Author keywords

Bayesian inference; Feature extraction; Mixture of Principal Component Analyzers; Model selection; Variational Bayes method

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; INTEGRATION; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; PROBLEM SOLVING;

EID: 0141526271     PISSN: 08821666     EISSN: None     Source Type: Journal    
DOI: 10.1002/scj.10394     Document Type: Article
Times cited : (3)

References (14)
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  • 2
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  • 5
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    • On-line model selection based on the variational Bayes
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    • Sato, M.1
  • 6
    • 84898934543 scopus 로고    scopus 로고
    • Variational inference for Bayesian mixture of factor analyzers
    • Ghahramani Z, Beal MJ. Variational inference for Bayesian mixture of factor analyzers. Adv Neural Inf Process Syst 2000;12:449-455.
    • (2000) Adv Neural Inf Process Syst , vol.12 , pp. 449-455
    • Ghahramani, Z.1    Beal, M.J.2
  • 9
    • 0000208683 scopus 로고
    • Stochastic complexity
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    • Rissanen, J.1
  • 10
    • 0036887504 scopus 로고    scopus 로고
    • Bayesian model search for mixture models based on optimizing variational bounds
    • Ueda N, Ghahramani Z. Bayesian model search for mixture models based on optimizing variational bounds. Neural Networks 2002;15:1223-1242.
    • (2002) Neural Networks , vol.15 , pp. 1223-1242
    • Ueda, N.1    Ghahramani, Z.2
  • 13
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    • A view of the EM algorithm that justifies incremental, sparse, and other variants
    • Jordan MI, et al (editors). Kluwer Academic
    • Neal RM, Hinton GE. A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Jordan MI, et al (editors). Learning in graphical models. Kluwer Academic; 1999. p 355-368.
    • (1999) Learning in Graphical Models , pp. 355-368
    • Neal, R.M.1    Hinton, G.E.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.