Mixture of the robust L1 distributions and its applications

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4830 LNAI, Issue , 2007, Pages 26-35

  Gao, Junbin ^a   Xu, Richard Y ^a  

^a CHARLES STURT UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN NOISE (ELECTRONIC); LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBABILITY;

DATA OUTLIERS; GAUSSIAN DENSITIES; MODEL LEARNING; SUPERPOSITION;

ROBUST CONTROL;

EID: 38349060081     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-76928-6_5     Document Type: Conference Paper

References (20)

1. Gao, J.: Robust L1 principal component analysis and its bayesian variational inference. Neural Computation (to appear, 2008)
2. McLachlan, G., Peel, D.: Finite Mixture Models. John Wiley, New York (2000)
3. Dempster, A., Laird, N., Rubin, D.: Maximum-likelihood from incomplete data via the EM algorithm. J. Royal Statistical Soceity, Ser. B 39, 1-38 (1977)
4. Tipping, M., Bishop, C.: Mixtures of probabilistic principal component analyzers. Neural Computation 11, 443-482 (1999)
5. Verbeek, J.: Learning nonlinear image manifolds by global alignment of local linear models. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(8), 1236-1250 (2006)
6. Bhowmick, D., Davison, A., Goldstein, D., Ruffieux, Y.: A Laplace mixture model for identification of differential expression in microarray experiments. Biostatistics 7, 630-641 (2006)
7. Jordan, M.: Graphical models. Statistical Science (Special Issue on Bayesian Statistics) 19, 140-155 (2004)
8. Peel, D., McLachlan, G.: Robust mixture modelling using the t distribution. Statistic and Computing 10, 339-348 (2000)
9. Ridder, D.D., Franc, V.: Robust subspace mixture models using t-distributions. In: Harvey, R., Bangham, A. (eds.) BMVC 2003. Proceedings of the 14th British Machine Vision Conference, pp. 319-328 (2003)
10. Archambeau, C.: Probabilistic models in noisy environments and their application to a visual prosthesis for the blind. Doctoral dissertation, Université Catholique de Louvain, Belgium (2005)
11. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Royal. Statist. Soc B. 58, 267-288 (1996)
12. Ng, A.: Feature selection, L1 vs. L2 regularization, and rotational invariance. In: Proceedings of Intl Conf. Machine Learning (2004)
13. Jolliffe, I.: Principal component analysis, 2nd edn. Springer, New York (2002)
14. Zou, H., Hastie, T., Tibshirani, R.: Sparse principal component analysis. Technical report, Statistics Department, Stanford University (2004)
15. Park, H.J., Lee, T.W.: Modeling nonlinear dependencies in natural images using mixture of laplacian distribution. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 17, pp. 1041-1048. MIT Press, Cambridge (2005)
16. Gao, J., Gunn, S., Kandola, J.: Adapting kernels by variational approach in svm. In: McKay, B., Slaney, J.K. (eds.) AI 2002. LNCS (LNAI), vol. 2557, pp. 395-406. Springer, Heidelberg (2002)
17. Tipping, M., Lawrence, N.: Variational inference for Student-t models: Robust Bayesian interpolation and generalized component analysis. NeuroComputing 69, 123-141 (2005)
18. Pontil, M., Mukherjee, S., Girosi, F.: On the noise model of support vector machine regression. In: A.I. Memo 1651, AI Laboratory, MIT, Cambridge (1998)
19. Guo, Y., Gao, J.B., Kwan, P.W.: Kernel Laplacian eigenmaps for visualization of non-vectorial data. In: Sattar, A., Kang, B.-H. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1179-1183. Springer, Heidelberg (2006)
20. Guo, Y., Gao, J.B., Kwan, P.W.: Visualization of non-vectorial data using twin kernel embedding. In: Ong, K., Smith-Miles, K., Lee, V., Ng, W. (eds.) AIDM 2006. Proceedings of the International Workshop on Integrating AI and Data Mining, pp. 11-17. IEEE Computer Society Press, Los Alamitos (2006)