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Mixture Models, Latent Variables and the EM Algorithm

Mixture Models, Latent Variables and the EM Algorithm

陈傲天
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1850
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Compressing and restricting density estimates. Mixtures of limited numbers of distributions. Mixture models as probabilistic clustering; finally an answer to "how many clusters?" The EM algorithm as an iterative way of maximizing likelihood with latent variables. Analogy to k-means. More theory of the EM algorithm. Applications: density mixtures, signal processing/state estimation, mixtures of regressions, mixtures of experts; topic models and probabilistic latent semantic analysis. A glance at non-parametric mixture models.
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