A mixture probabilistic PCA model for multivariate processes monitoring

Proceedings of the American Control Conference

Volumn 4, Issue , 2004, Pages 3111-3115

  Zhang, Feng ^a  

^a Texas AandM University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; NONLINEAR SYSTEMS; PROBABILITY; PROCESS CONTROL; VECTORS;

IN-PROCESS SENSING; LATENT VARIABLE MODELS; NONLINEAR DATA DENSITY;

PRINCIPAL COMPONENT ANALYSIS;

EID: 8744300769     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.23919/acc.2004.1384387     Document Type: Conference Paper

References (8)

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3. Mario A. T. Figueiredo and A. K. Jain "Unsupervised Learning of Finite Mixture Models," IEEE Trans. on P.A.M.I., 24(3), 381-396, 2002.
4. C. E. McCulloch, "Maximum Likelihood Algorithm for Generalized Linear Mixed Models," J.A.S.A., 92, 162-170, 1997.
5. G. McLachlan and D. Peel, Finite Mixture Models. New York: John Wiley & Sons, 2000.
6. S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, 290, 2323-2326, 2000.
7. M. E. Tipping and C. M. Bishop, "Mixtures of Probabilistic Principal Component Analyzers," Neural Computation, 11(2), 443-482, 1999.
8. J. E. Jackson, A User's Guide to Principal Components, Wiley: New York, 1991.