Mixtures of robust probabilistic principal component analyzers

ESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks

Volumn , Issue , 2007, Pages 229-234

  Archambeau, Cédric ^a   Delannay, Nicolas ^b   Verleysen, Michel ^b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

CURSE OF DIMENSIONALITY; HIGH DIMENSIONAL SPACES; LOCAL LINEAR MODELS; LOW-DIMENSIONAL MANIFOLDS; MIXTURE OF GAUSSIANS; PRINCIPAL DIRECTIONS; PRINCIPAL ORIENTATIONS; PROBABILISTIC PRINCIPAL COMPONENT ANALYZERS;

MIXTURES; NEURAL NETWORKS;

DATA MINING;

EID: 84887008371     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Ian T. Jolliffe. Principal Component Analysis. Springer-Verlag, New York, 1986.
2. C. Archambeau, N. Delannay, and M. Verleysen. Robust probabilistic projections. In W. W. Cohen and A. Moore, editors, ICML 23, pages 33-40. ACM, 2006.
3. S. T. Roweis. EM algorithms for PCA and SPCA. In M. I. Jordan, M. J. Kearns, and S. A. Solla, editors, NIPS 10. The MIT Press, 1998.
4. M. E. Tipping and C. M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society B, 61:611-622, 1999.
5. D. Peel and G. J. McLachlan. Robust mixture modelling using the t distribution. Statistics and Computing, 10:339-348, 2000.
6. L. Xu and A. L. Yuille. Robust principal component analysis by self-organizing rules based on statistical physics approach. IEEE trans. Neural Networks, 6(1):131-143, 1995.
7. K. Huang, Y. Ma, and R. Vidal. Minimum effective dimension for mixtures of subspaces: A robust GPCA algorithm and its applications. In CVPR 2004, vol 2, pages 631-638.
8. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via EM algorithm. Journal of the Royal Statistical Society B, 39(1):1-38, 1977.
9. J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319-2323, 2000.
10. M. E. Tipping and C. M. Bishop. Mixtures of probabilistic principal component analyzers. Neural Computation, 11(2):443-482, 1999.
11. C. Liu and D. B. Rubin. ML estimation of the t distribution using EM and its extensions, ECM and ECME. Statistica Sinica, 5:19-39, 1995.