A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Mixtures of probabilistic principal component analyzers
Neural Computation
Very fast EM-based mixture model clustering using multiresolution kd-trees
Proceedings of the 1998 conference on Advances in neural information processing systems II
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Learning Mixtures of Gaussians
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
SMEM Algorithm for Mixture Models
Neural Computation
A kurtosis-based dynamic approach to Gaussian mixture modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Mixture of experts classification using a hierarchical mixture model
Neural Computation
Efficient greedy learning of Gaussian mixture models
Neural Computation
A Bayesian Regularization Method for the Probabilistic RBF Network
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Learning Vector Quantization for Multimodal Data
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Clustering ensembles of neural network models
Neural Networks
A clustering method based on boosting
Pattern Recognition Letters
An empirical comparison of EM, SEM and MCMC performance for problematic Gaussian mixture likelihoods
Statistics and Computing
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
Web-based text classification in the absence of manually labeled training documents
Journal of the American Society for Information Science and Technology
Performance of data resampling methods for robust class discovery based on clustering
Intelligent Data Analysis
New signal decomposition method based speech enhancement
Signal Processing
Probabilistic Models Based on the Π-Sigmoid Distribution
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
A Sparse Regression Mixture Model for Clustering Time-Series
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Fast Simplex Optimization for Active Appearance Model
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Image denoising using mixtures of projected Gaussian scale mixtures
IEEE Transactions on Image Processing
Learning the number of Gaussian cusing hypothesis test
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Learning Gaussian mixture models with entropy-based criteria
IEEE Transactions on Neural Networks
Split-merge incremental learning (SMILE) of mixture models
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A greedy merge learning algorithm for Gaussian mixture model
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Active learning schemes for reduced dimensionality hyperspectral classification
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Multimedia Tools and Applications
EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing
Computational Intelligence and Neuroscience - Special issue on academic software applications for electromagnetic brain mapping using MEG and EEG
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
Gossip-Based greedy gaussian mixture learning
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
A dynamic merge-or-split learning algorithm on gaussian mixture for automated model selection
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
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
Using registration uncertainty visualization in a user study of a simple surgical task
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Text classification using web corpora and EM algorithms
AIRS'04 Proceedings of the 2004 international conference on Asian Information Retrieval Technology
Image segmentation for robots: fast self-adapting gaussian mixture model
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Applying spatial distribution analysis techniques to classification of 3D medical images
Artificial Intelligence in Medicine
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Learning a Gaussian mixture with a local algorithm like EM can be difficult because (i) the true number of mixing components is usually unknown, (ii) there is no generally accepted method for parameter initialization, and (iii) the algorithm can get trapped in one of the many local maxima of the likelihood function. In this paper we propose a greedy algorithm for learning a Gaussian mixture which tries to overcome these limitations. In particular, starting with a single component and adding components sequentially until a maximum number k, the algorithm is capable of achieving solutions superior to EM with k components in terms of the likelihood of a test set. The algorithm is based on recent theoretical results on incremental mixture density estimation, and uses a combination of global and local search each time a new component is added to the mixture.