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Volumn 2006, Issue , 2006, Pages 33-40

Robust probabilistic projections

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CORRELATION METHODS; DATA MINING; LEARNING SYSTEMS; MAXIMUM LIKELIHOOD ESTIMATION; PRINCIPAL COMPONENT ANALYSIS; ROBUSTNESS (CONTROL SYSTEMS);

EID: 33749236995     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (34)

References (15)
  • 3
    • 0034849799 scopus 로고    scopus 로고
    • Robust principal component analysis for computer vision
    • de la Torre, F., & Black, M. J. (2001). Robust principal component analysis for computer vision. Int. Conf. on Computer Vision (pp. 362-369).
    • (2001) Int. Conf. on Computer Vision , pp. 362-369
    • Torre, F.1    Black, M.J.2
  • 5
    • 58149421595 scopus 로고
    • Analysis of a complex of statistical variables into principal components
    • Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 417-441.
    • (1933) Journal of Educational Psychology , vol.24 , pp. 417-441
    • Hotelling, H.1
  • 6
    • 0000107975 scopus 로고
    • Relations between two sets of variates
    • Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28, 321-377.
    • (1936) Biometrika , vol.28 , pp. 321-377
    • Hotelling, H.1
  • 8
    • 0002864973 scopus 로고
    • ML estimation of the t distribution using em and its extensions, ECM and ECME
    • Liu, C., & Rubin, D. B. (1995). ML estimation of the t distribution using EM and its extensions, ECM and ECME. Statistica Sinica, 5, 19-39.
    • (1995) Statistica Sinica , vol.5 , pp. 19-39
    • Liu, C.1    Rubin, D.B.2
  • 9
    • 0002788893 scopus 로고    scopus 로고
    • A view of the em algorithm that justifies incremental, sparse, and other variants
    • M. I. Jordan (Ed.). Kluwer
    • Neal, R. M., & Hinton, G. E. (1998). A view of the EM algorithm that justifies incremental, sparse, and other variants. In M. I. Jordan (Ed.), Learning in graphical models, 355-368. Kluwer.
    • (1998) Learning in Graphical Models , pp. 355-368
    • Neal, R.M.1    Hinton, G.E.2
  • 10
    • 0041407143 scopus 로고    scopus 로고
    • Robust mixture modelling using the t distribution
    • Peel, D., & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing, 10, 339-348.
    • (2000) Statistics and Computing , vol.10 , pp. 339-348
    • Peel, D.1    McLachlan, G.J.2
  • 12
    • 0033556788 scopus 로고    scopus 로고
    • Mixtures of probabilistic principal component analyzers
    • Tipping, M. E., & Bishop, C. M. (1999a). Mixtures of probabilistic principal component analyzers. Neural Computation, 11, 443-482.
    • (1999) Neural Computation , vol.11 , pp. 443-482
    • Tipping, M.E.1    Bishop, C.M.2
  • 15
    • 0029184173 scopus 로고
    • Robust principal component analysis by self-organizing rules based on statistical physics approach
    • Xu, L., & Yuille, A. L. (1995). Robust principal component analysis by self-organizing rules based on statistical physics approach. IEEE Transactions on Neural Networks, 6, 131-143.
    • (1995) IEEE Transactions on Neural Networks , vol.6 , pp. 131-143
    • Xu, L.1    Yuille, A.L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.