Robust clustering by deterministic agglomeration EM of mixtures of multivariate t-distributions

Pattern Recognition

Volumn 35, Issue 5, 2002, Pages 1127-1142

  Shoham, Shy ^a  

^a Department of Electrical and Computer Engineering   (United States)

Author keywords

Agglomerative algorithms; Clustering; Deterministic annealing; EM algorithm; Finite mixture models; Robust algorithms; T distribution

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION;

EXPECTATION-MAXIMIZATION (EM) APPROACH;

PATTERN RECOGNITION;

DATA PROCESSING;

EID: 0036568162     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(01)00080-2     Document Type: Article

References (33)

1. Statistical pattern recognition: A review
2. Maximum likelihood from incomplete data using the EM algorithm
3. Deterministic annealing EM algorithm
4. Determination of the number of components in Gaussian mixtures using agglomerative clustering
5. Robust statistical modeling using the t distribution
6. Robust mixture modelling using the t distribution
7. Deterministic annealing for clustering, compression, classification, regression, and related optimization problems
8. A view of the EM algorithm that justifies incremental, sparse, and other variants
9. Deterministic annealing: Fast physical heuristics for real time optimization of large systems
10. SMEM algorithm for mixture models
11. Clustering by competitive agglomeration
12. Categorization of image databases for efficient retrieval using robust mixture decomposition
13. Unsupervised selection and estimation of finite mixture models
14. Fast global optimization fuzzy-neural network and its application in data fusion
15. Bayesian approaches to Guassian mixture modeling
16. On fitting mixture models
17. Model-based Gaussian and non-Gaussian clustering
18. Algorithms for model-based Gaussian hierarchical clustering
19. Structure learning in conditional probability models via entropic prior and parameter extinction
20. Robust clustering methods: A unified view
21. ML estimation of the t distribution using EM and its extensions, ECM and ECME
22. Mixture models and atypical values
23. Robust parameter estimation for mixture model
24. The EM algorithm - An old folk-song sung to a fast new tune
25. A curious likelihood identity for the multivariate t-distribution
26. ML estimation of the multivariate t distribution and the EM algorithm
27. Parameter expansion to accelerate EM: The PX-EM algorithm
28. Deterministic annealing for density estimation by multivariate normal mixtures