Vector quantization and signal compression
Vector quantization and signal compression
A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Vector Space Projections: A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics
Image Segmentation and Compression Using Hidden Markov Models
Image Segmentation and Compression Using Hidden Markov Models
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
On a Parameter Estimation Method for Gibbs-Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Convex Optimization
Wireless Communications
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Journal of Global Optimization
A First Course in Information Theory (Information Technology: Transmission, Processing and Storage)
A First Course in Information Theory (Information Technology: Transmission, Processing and Storage)
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
The application of hidden Markov models in speech recognition
Foundations and Trends in Signal Processing
Gradient estimation in global optimization algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Probabilistic modeling of traffic lanes from GPS traces
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Maximum Likelihood Estimation of Compound-Gaussian Clutter and Target Parameters
IEEE Transactions on Signal Processing
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
On the optimality of conditional expectation as a Bregman predictor
IEEE Transactions on Information Theory
Functional Bregman Divergence and Bayesian Estimation of Distributions
IEEE Transactions on Information Theory
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Expectation-maximization algorithms, null spaces, and MAP image restoration
IEEE Transactions on Image Processing
Multi-task regularization of generative similarity models
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Adaptive splitting and selection algorithm for classification of breast cytology images
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
The construction of causal networks to estimate coral bleaching intensity
Environmental Modelling & Software
Evaluating the crowd with confidence
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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This introduction to the expectation–maximization (EM) algorithm provides an intuitive and mathematically rigorous understanding of EM. Two of the most popular applications of EM are described in detail: estimating Gaussian mixture models (GMMs), and estimating hidden Markov models (HMMs). EM solutions are also derived for learning an optimal mixture of fixed models, for estimating the parameters of a compound Dirichlet distribution, and for dis-entangling superimposed signals. Practical issues that arise in the use of EM are discussed, as well as variants of the algorithm that help deal with these challenges.