A countably infinite mixture model for clustering and feature selection

Knowledge and Information Systems

Volumn 33, Issue 2, 2012, Pages 351-370

  Bouguila, Nizar ^a   Ziou, Djemel ^b  

^a Concordia University ^*  (Canada)

^b UNIVERSITÉ DE SHERBROOKE   (Canada)

Author keywords

Categorization; Clustering; Dirichlet process; Feature selection; Generalized Dirichlet; MCMC; Mixture models; Non parametric Bayesian methods

Indexed keywords

BAYESIAN NETWORKS; DATA MINING; MARKOV CHAINS; MIXTURES;

CATEGORIZATION; CLUSTERING; DIRICHLET PROCESS; GENERALIZED DIRICHLET; MCMC; MIXTURE MODEL; NON-PARAMETRIC BAYESIAN;

FEATURE EXTRACTION;

EID: 84867734798     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-011-0467-4     Document Type: Article

References (38)

1. McLachlan GJ, Peel D (2000) Finite mixture models. Wiley, New York.
2. Bouguila N, Ziou D (2006) Unsupervised selection of a finite Dirichlet mixture model: an MML-based approach. IEEE Trans Knowl Data Eng 18(8): 993-1009.
3. Bouguila N, Ziou D (2007) High-dimensional unsupervised selection and estimation of a finite generalized Dirichlet mixture model based on minimum message length. IEEE Trans Pattern Anal Mach Intell 29(10): 1716-1731.
4. Zhang M, Alhajj R (2010) Effectiveness of NAQ-tree as index structure for similarity search in high-dimensional metric space. Knowl Inf Syst 22: 1-21.
5. Moise G, Zimek A, Kröger P, Kriegel H-P, Sander J (2009) Subspace and projected clustering: experimental evaluation and analysis. Knowl Inf Syst 21: 299-326.
6. Lu J, Li R, Zhang Y, Zhao T, Lu Z (2010) Image annotation technique based on feature selection for class-pairs. Knowl Inf Syst 24(2): 325-337.
7. Bouguila N, Ziou D (2009) A non-parametric Bayesian learning model: application to text and image categorization. In: Proceedings of the 13th Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD). Springer, LNAI 5476, pp 463-474.
8. Ferguson TS (1983) Bayesian density estimation by mixtures of normal distributions. In: Rizvi H, Rustagi J (eds) Recent advances in statistics. Academic Press, New York, pp 287-302.
9. Escobar MD, West M (1995) Bayesian density estimation and inference using mixtures. J Am Stat Assoc 90(430): 577-588.
10. Neal RM (2000) Markov Chain sampling methods for Dirichlet process mixture models. J Comput Graph Stat 9: 249-265.
11. Ghosh JK, Ramamoorthi RV (2003) Bayesian nonparametrics. Springer, Berlin.
12. Teh YW, Jordan MI, Beal MI, Matthew J, Blei DM (2006) Hierarchical Dirichlet processes. J Am Stat Assoc 101(476): 1566-1581.
13. Rasmussen CE (2000) The infinite gaussian mixture model. In: Advances in neural information processing systems (NIPS), pp 554-560.
14. Bouguila N, Ziou D (2004) A powerful finite mixture model based on the generalized Dirichlet distribution: unsupervised learning and applications. In: Proceedings of the 17th international conference on pattern recognition (ICPR), pp 280-283.
15. Bouguila N (2008) Clustering of count data using generalized Dirichlet multinomial distributions. IEEE Trans Knowl Data Eng 20(4): 462-474.
16. Bouguila N, Ziou D, Hammoud RI (2009) On Bayesian analysis of a finite generalized Dirichlet mixture via a metropolis-within-gibbs sampling. Pattern Anal Appl 12(2): 151-166.
17. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm (with discussion). J Royal Stat Soc B 39: 1-38.
18. Bouguila N, Ziou D (2006) A hybrid SEM algorithm for high-dimensional unsupervised learning using a finite generalized Dirichlet mixture. IEEE Trans Image Process 15(9): 2657-2668.
19. Boutemedjet S, Bouguila N, Ziou D (2009) A hybrid feature extraction selection approach for high-dimensional non-gaussian data clustering. IEEE Trans Pattern Anal Mach Intell 31(9): 1429-1443.
20. Bouguila N, Ziou D, Monga E (2006) Practical Bayesian estimation of a finite beta mixture through gibbs sampling and its applications. Stat Comput 16(2): 215-225.
21. Gelman A, Carlin JB, Stern HS, Rubin DB (2003) Bayesian data analysis, 2nd edn. Chapman & Hall/CRC, London.
22. Marin J-M, Robert CP (2007) Bayesian core: a practical approach to computational Bayesian statistics. Springer, Berlin.
23. Ishwaran H, James LF (2003) Generalized weighted chinese restaurant processes for species sampling mixture models. Stat Sinica 13: 1211-1235.
24. Papaspiliopoulos O, Roberts GO (2008) Retrospective Markov Chain Monte Carlo methods for Dirichlet process hierarchical models. Stat Sinica 95(1): 169-186.
25. Carlin BP, Louis TA (2000) Bayes and empirical Bayes methods for data analysis, second edition. Chapman & Hall/CRC, London.
26. Gilks WR, Wild P (1993) Algorithm aS 287: adaptive rejection sampling from log-concave density functions. Appl Stat 42(4): 701-709.
27. Chib S, Greenberg E (1995) Understanding the metropolis-hastings algorithm. Am Stat 49(4): 327-335.
28. Bouguila N, Ziou D (2004) Dirichlet-based probability model applied to human skin detection. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp 521-524.
29. Madsen RE, Kauchak D, Elkan C (2005) Modeling word burstiness using the Dirichlet distribution. In: Proceedings of the 22nd international conference on machine learning (ICML), pp 545-552.
30. McCallum AK (1996) Bow: a toolkit for statistical language modeling, text retrieval, classification and clustering. Technical report.
31. Gong Z, Liu Q (2009) Improving keyword based web image search with visual feature distribution and term expansion. Knowl Inf Syst 21: 113-132.
32. Bartolini I, Ciaccia P, Patella M (2009) Query processing issues in region-based image databases. Knowl Inf Syst. In press.
33. Bouguila N, Ziou D, Vaillancourt J (2003) Novel mixtures based on the Dirichlet distribution: application to data and image classification. In: Machine learning and data mining in pattern recognition (MLDM), LNAI 2734. pp 172-181.
34. Csurka G, Dance CR, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, 8th European conference on computer vision (ECCV).
35. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3): 145-175.
36. Fei-Fei L, Perona P (2005) A Bayesian hierarchical model for learning natural scene categories. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition (CVPR), pp 524-531.
37. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2169-2178.
38. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2): 91-110.