Bootstrap technique in cluster analysis
Pattern Recognition
Algorithms for clustering data
Algorithms for clustering data
Statistical physics, mixtures of distributions, and the EM algorithm
Neural Computation
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Deterministic annealing EM algorithm
Neural Networks
Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Inference in model-based cluster analysis
Statistics and Computing
Mixfit: An Algorithm for the Automatic Fitting and Testing of Normal Mixture Models
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Pairwise Clustering with Matrix Factorisation and the EM Algorithm
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Recognizing Indoor Images with Unsupervised Segmentation and Graph Matching
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Designing the Minimal Structure of Hidden Markov Model by Bisimulation
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Learning Mixture Models for Gender Classification Based on Facial Surface Normals
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Discrete visual features modeling via leave-one-out likelihood estimation and applications
Journal of Visual Communication and Image Representation
Entropy-based variational scheme for fast bayes learning of Gaussian mixtures
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Interactive and real-time generation of home video summaries on mobile devices
IMMPD '11 Proceedings of the 2011 international ACM workshop on Interactive multimedia on mobile and portable devices
Gender classification using principal geodesic analysis and gaussian mixture models
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Two entropy-based methods for learning unsupervised gaussian mixture models
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Color image segmentation through unsupervised gaussian mixture models
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
MML-Based approach for finite dirichlet mixture estimation and selection
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Trimap segmentation for fast and user-friendly alpha matting
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
A multi-classifier approach to face image segmentation for travel documents
Expert Systems with Applications: An International Journal
Deriving kernels from generalized Dirichlet mixture models and applications
Information Processing and Management: an International Journal
Naive possibilistic classifiers for imprecise or uncertain numerical data
Fuzzy Sets and Systems
| Hi-index | 0.00 |
Consider the problem of fitting a finite Gaussian mixture, with an unknown number of components, to observed data. This paper proposes a new minimum description length (MDL) type criterion, termed MMDL(f or mixture MDL), to select the number of components of the model. MMDLis based on the identification of an "equivalent sample size", for each component, which does not coincide with the full sample size. We also introduce an algorithm based on the standard expectation-maximization (EM) approach together with a new agglomerative step, called agglomerative EM (AEM). The experiments here reported have shown that MMDLo utperforms existing criteria of comparable computational cost. The good behavior of AEM, namely its good robustness with respect to initialization, is also illustrated experimentally.