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• W2784269077 abstract "We present a noise-injected version of the Expectation-Maximization (EM) algorithm: the Noisy Expectation Maximization (NEM) algorithm. The NEM algorithm uses noise to speed up the convergence of the EM algorithm. The NEM theorem shows that injected noise speeds up the average convergence of the EM algorithm to a local maximum of the likelihood surface if a positivity condition holds. The generalized form of the noisy expectation-maximization (NEM) algorithm allow for arbitrary modes of noise injection including adding and multiplying noise to the data. We demonstrate these noise benefits on EM algorithms for the Gaussian mixture model (GMM) with both additive and multiplicative NEM noise injection. A separate theorem (not presented here) shows that the noise benefit for independent identically distributed additive noise decreases with sample size in mixture models. This theorem implies that the noise benefit is most pronounced if the data is sparse. Injecting blind noise only slowed convergence." @default.
• W2784269077 created "2018-01-26" @default.
• W2784269077 creator A5047871627 @default.
• W2784269077 creator A5065383312 @default.
• W2784269077 date "2018-01-12" @default.
• W2784269077 modified "2023-09-27" @default.
• W2784269077 title "Noisy Expectation-Maximization: Applications and Generalizations" @default.
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• W2784269077 hasPublicationYear "2018" @default.
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