A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Deterministic annealing EM algorithm
Neural Networks
An Experimental Comparison of Model-Based Clustering Methods
Machine Learning
Editorial: recent developments in mixture models
Computational Statistics & Data Analysis
Initializing EM using the properties of its trajectories in Gaussian mixtures
Statistics and Computing
Nested Monte Carlo EM Algorithm for Switching State-Space Models
IEEE Transactions on Knowledge and Data Engineering
A mixture of mixture models for a classification problem: The unity measure error
Computational Statistics & Data Analysis
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
Parabolic acceleration of the EM algorithm
Statistics and Computing
Initializing Partition-Optimization Algorithms
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computer Vision and Image Understanding
A mixture model-based approach to the clustering of exponential repeated data
Journal of Multivariate Analysis
Mixture-model cluster analysis using information theoretical criteria
Intelligent Data Analysis
Model-based cluster and discriminant analysis with the MIXMOD software
Computational Statistics & Data Analysis
Learning mixture models via component-wise parameter smoothing
Computational Statistics & Data Analysis
Acceleration of the EM algorithm via extrapolation methods: Review, comparison and new methods
Computational Statistics & Data Analysis
Journal of Visual Communication and Image Representation
Mixtures of GAMs for habitat suitability analysis with overdispersed presence/absence data
Computational Statistics & Data Analysis
Random swap EM algorithm for finite mixture models in image segmentation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Texture classification by modeling joint distributions of local patterns with Gaussian mixtures
IEEE Transactions on Image Processing
Robust mixture modeling based on scale mixtures of skew-normal distributions
Computational Statistics & Data Analysis
Genetic algorithms in partitional clustering: a comparison
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Expert Systems with Applications: An International Journal
A finite mixture model for multivariate counts under endogenous selectivity
Statistics and Computing
Expert Systems with Applications: An International Journal
Simultaneous model-based clustering and visualization in the Fisher discriminative subspace
Statistics and Computing
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
Initializing the EM algorithm in Gaussian mixture models with an unknown number of components
Computational Statistics & Data Analysis
Theoretical and practical considerations on the convergence properties of the Fisher-EM algorithm
Journal of Multivariate Analysis
A robust EM clustering algorithm for Gaussian mixture models
Pattern Recognition
Computational aspects of fitting mixture models via the expectation-maximization algorithm
Computational Statistics & Data Analysis
Acceleration of the EM algorithm: P-EM versus epsilon algorithm
Computational Statistics & Data Analysis
Preliminary estimators for a mixture model of ordinal data
Advances in Data Analysis and Classification
Random swap EM algorithm for Gaussian mixture models
Pattern Recognition Letters
Using evolutionary algorithms for model-based clustering
Pattern Recognition Letters
Dealing with multiple local modalities in latent class profile analysis
Computational Statistics & Data Analysis
Finite mixtures of unimodal beta and gamma densities and the $$k$$-bumps algorithm
Computational Statistics
Using conditional independence for parsimonious model-based Gaussian clustering
Statistics and Computing
A multivariate linear regression analysis using finite mixtures of t distributions
Computational Statistics & Data Analysis
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Simple methods to choose sensible starting values for the EM algorithm to get maximum likelihood parameter estimation in mixture models are compared. They are based on random initialization, using a classification EM algorithm (CEM), a Stochastic EM algorithm (SEM) or previous short runs of EM itself. Those initializations are included in a search/run/select strategy which can be compounded by repeating the three steps. They are compared in the context of multivariate Gaussian mixtures on the basis of numerical experiments on both simulated and real data sets in a target number of iterations. The main conclusions of those numerical experiments are the following. The simple random initialization which is probably the most employed way of initiating EM is often outperformed by strategies using CEM, SEM or shorts runs of EM before running EM. Also, it appears that compounding is generally profitable since using a single run of EM can often lead to suboptimal solutions. Otherwise, none of the experimental strategies can be regarded as the best one and it is difficult to characterize situations where a particular strategy can be expected to outperform the other ones. However, the strategy initiating EM with short runs of EM can be recommended. This strategy, which as far as we know was not used before the present study, has some advantages. It is simple, performs well in a lot of situations presupposing no particular form of the mixture to be fitted to the data and seems little sensitive to noisy data.