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On-line expectation-maximization algorithm for latent data models
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DOI：10.1111/j.1467-9868.2009.00698.x.png)
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Abstract
En 中文
We propose a generic on-line (also sometimes called adaptive or recursive) version of the expectation-maximization (EM) algorithm applicable to latent variable models of independent observations. Compared with the algorithm of Titterington, this approach is more directly connected to the usual EM algorithm and does not rely on integration with respect to the complete-data distribution. The resulting algorithm is usually simpler and is shown to achieve convergence to the stationary points of the Kullback-Leibler divergence between the marginal distribution of the observation and the model distribution at the optimal rate, i.e. that of the maximum likelihood estimator. In addition, the approach proposed is also suitable for conditional (or regression) models, as illustrated in the case of the mixture of linear regressions model.
Keywords:
Adaptive algorithms
Expectation-maximization
Latent data models
Mixture of regressions
On-line estimation
Polyak-Ruppert averaging
Stochastic approximation
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