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What is EM Algorithm

Chapter 11
Handbook of Research on Systems Biology Applications in Medicine
A method for calculating maximum likelihood estimates of parameters in statistical models in situations where the observed data can be usefully viewed as being incomplete. It proceeds by consideration of the complete data log likelihood, which is formed on the basis of the complete data. The latter comprises the observed data and the ‘missing data’. It is implemented iteratively by alternating two steps known as the expectation (E) step and the maximization (M) step. On the E-step the Q-function is calculated by averaging the complete data log likelihood over the conditional distribution of the complete data given the observed data, using the current value for the parameter vector. This is followed by the M-step in which the current estimate of the parameter vector is updated to that value which globally maximizes the Q-function.
Published in Chapter:
Clustering Methods for Gene-Expression Data
L.K. Flack (University of Queensland, Australia)
Copyright: © 2009 |Pages: 12
DOI: 10.4018/978-1-60566-076-9.ch011
Abstract
Clustering methods are used to place items in natural patterns or convenient groups. They can be used to place genes into clusters to have similar expression patterns across the tissue samples of interest. They can also be used to cluster tissues into groups on the basis of their gene profiles. Examples of the methods used are hierarchical agglomerative clustering, k-means clustering, self organizing maps, and model-based methods. The focus of this chapter is on using mixtures of multivariate normal distributions to provide model-based clusterings of tissue samples and of genes.
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Chapter 7
Improving Techniques for Naïve Bayes Text Classifiers
An iterative method for estimating maximum likelihood in problems with incomplete (or unlabeled) data.
Published in Chapter: Improving Techniques for Naïve Bayes Text Classifiers; From: Handbook of Research on Text and Web Mining Technologies
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Chapter 22
Statistical Machine Learning Approaches for Sports Video Mining Using Hidden Markov Models
a statistical estimation algorithm that can find maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables.
Published in Chapter: Statistical Machine Learning Approaches for Sports Video Mining Using Hidden Markov Models; From: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
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