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Volumn 5012 LNAI, Issue , 2008, Pages 527-535

Improving the robustness to outliers of mixtures of probabilistic PCAs

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; GAUSSIAN NOISE (ELECTRONIC); HIERARCHICAL SYSTEMS; MAXIMUM LIKELIHOOD; PROBABILISTIC LOGICS; ROBUSTNESS (CONTROL SYSTEMS);

EID: 44649201168     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-68125-0_47     Document Type: Conference Paper
Times cited : (6)

References (15)
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    • Peel, D., McLachlan, G.J.: Robust mixture modelling using the t distribution. Statistics and Computing 10, 339-348 (2000)
    • (2000) Statistics and Computing , vol.10 , pp. 339-348
    • Peel, D.1    McLachlan, G.J.2
  • 9
    • 18244378520 scopus 로고    scopus 로고
    • On bayesian analysis of mixtures with an unknown number of components
    • Richardson, S., Green, P.: On bayesian analysis of mixtures with an unknown number of components. J. Roy. Statist. Soc. 59, 731-792 (1996)
    • (1996) J. Roy. Statist. Soc , vol.59 , pp. 731-792
    • Richardson, S.1    Green, P.2
  • 10
    • 84898929664 scopus 로고    scopus 로고
    • EM algorithms for PCA and SPCA
    • Jordan, M.I, Kearns, M.J, Solla, S.A, eds, MIT Press, Cambridge
    • Roweis, S.T.: EM algorithms for PCA and SPCA. In: Jordan, M.I., Kearns, M.J., Solla, S.A. (eds.) Advances in Neural Information Processing Systems 10 (NIPS). MIT Press, Cambridge (1998)
    • (1998) Advances in Neural Information Processing Systems 10 (NIPS)
    • Roweis, S.T.1
  • 11
    • 0036568162 scopus 로고    scopus 로고
    • Robust clustering by deterministic agglomeration EM of mixtures of multivariate t-distributions
    • Shoham, S.: Robust clustering by deterministic agglomeration EM of mixtures of multivariate t-distributions. Pattern Recognition 35(5), 1127-1142 (2002)
    • (2002) Pattern Recognition , vol.35 , Issue.5 , pp. 1127-1142
    • Shoham, S.1
  • 12
    • 0033556788 scopus 로고    scopus 로고
    • Mixtures of probabilistic principal component analyzers
    • Tipping, M.E., Bishop, C.M.: Mixtures of probabilistic principal component analyzers. Neural Computation 11(2), 443-482 (1999)
    • (1999) Neural Computation , vol.11 , Issue.2 , pp. 443-482
    • Tipping, M.E.1    Bishop, C.M.2
  • 14
    • 85156191859 scopus 로고    scopus 로고
    • Bayesian methods for mixtures of experts
    • Touretzky, D.S, et al, eds, MIT Press, Cambridge
    • Waterhouse, S., MacKay, D., Robinson, T.: Bayesian methods for mixtures of experts. In: Touretzky, D.S., et al. (eds.) Advances in Neural Information Processing Systems, vol. 8, pp. 351-357. MIT Press, Cambridge (1996)
    • (1996) Advances in Neural Information Processing Systems , vol.8 , pp. 351-357
    • Waterhouse, S.1    MacKay, D.2    Robinson, T.3
  • 15
    • 0029184173 scopus 로고
    • Robust principal component analysis by self-organizing rules based on statistical physics approach
    • Xu, L., Yuille, A.L.: Robust principal component analysis by self-organizing rules based on statistical physics approach. IEEE Transactions on Neural Networks 6(1), 131-143 (1995)
    • (1995) IEEE Transactions on Neural Networks , vol.6 , Issue.1 , pp. 131-143
    • Xu, L.1    Yuille, A.L.2


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