On-line expectation-maximization algorithm for latent data models
On-line expectation-maximization algorithm for latent data models
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DOI：
10.1111/j.1467-9868.2009.00698.x
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发表时间：
2009-01-01
影响因子：
5.8
通讯作者：
Moulines, Eric
中科院分区：
文献类型：
--
作者：
Cappe, Olivier;Moulines, Eric
我们提出了一个通用的在线（有时也称为自适应或递归）版本的期望最大化（EM）算法适用于独立观测的潜变量模型。与Titterington的算法相比，该方法与通常的EM算法更直接地联系在一起，并且不依赖于关于完全数据分布的积分。由此产生的算法通常是简单的，并实现收敛到固定点的Kullback-Leibler分歧之间的边缘分布的观察和模型分布在最佳的速度，即最大似然估计。此外，所提出的方法也适用于条件（或回归）模型，如混合线性回归模型的情况下所示。
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.