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, 2011 The Expectation-Maximization (EM) algorithm is a broadly applicable approach to the iterative computation of maximum likelihood estimates in a wide variety of incomplete-data problems. The EM algorithm has a number of desirable properties, such as its numerical stability, reliable global convergence, and simplicity of implementation. There are, however,
Ng, Shu-Kay +2 more
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Biometrics, 1993
Summary: A modification of the EM algorithm is proposed for situations in which the maximization of the ``complete data'' likelihood function does not have a closed-form solution. Self-consistency of the modified algorithm is established. Application to carcinogenicity experiments is illustrated, and the results of a simulation study comparing the ...
Rai, S. N., Matthews, D. E.
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Summary: A modification of the EM algorithm is proposed for situations in which the maximization of the ``complete data'' likelihood function does not have a closed-form solution. Self-consistency of the modified algorithm is established. Application to carcinogenicity experiments is illustrated, and the results of a simulation study comparing the ...
Rai, S. N., Matthews, D. E.
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An EM Algorithm for Capsule Regression
Neural Computation, 2021We investigate a latent variable model for multinomial classification inspired by recent capsule architectures for visual object recognition (Sabour, Frosst, & Hinton, 2017 ). Capsule architectures use vectors of hidden unit activities to encode the pose of visual objects in an image, and they use the lengths of these vectors to encode the ...
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Proceedings of the IEEE, 1988
The estimate-maximize (EM) algorithm is an iterative method for finding maximum-likelihood parameter estimates from incomplete data. The authors develop an extension of the EM algorithm that may be useful in accelerating the algorithm and in simplifying the computations involved. The extension works with an intermediate complete data specification, and
Mordechai Segal, Ehud Weinstein
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The estimate-maximize (EM) algorithm is an iterative method for finding maximum-likelihood parameter estimates from incomplete data. The authors develop an extension of the EM algorithm that may be useful in accelerating the algorithm and in simplifying the computations involved. The extension works with an intermediate complete data specification, and
Mordechai Segal, Ehud Weinstein
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EM*: An EM Algorithm for Big Data
2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016Existing data mining techniques, more particularly iterative learning algorithms, become overwhelmed with big data. While parallelism is an obvious and, usually, necessary strategy, we observe that both (1) continually revisiting data and (2) visiting all data are two of the most prominent problems especially for iterative, unsupervised algorithms like
Hasan Kurban +2 more
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Acceleration of the EM algorithm: P-EM versus epsilon algorithm
Computational Statistics & Data Analysis, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alain F. Berlinet, Christophe Roland
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Statistics and Computing, 2013
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