Approximation of the expectation-maximization algorithm for Gaussian mixture models on big data

Przyborowski, Mateusz; Ślęzak, Dominik

doi:10.1109/bigdata55660.2022.10020450

Cited by 1 publication

(1 citation statement)

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“…Wen H introduced asymmetric Gaussian mixture models into finite mixture models to simulate more complex asymmetric distributions [12]. Mateusz Przyborowski presented an approximate method for the parameter learning of Gaussian mixture models in large datasets using the EM algorithm [13].…”

Section: Introductionmentioning

confidence: 99%

Gaussian Mixture Model for Marine Reverberations

Sun,

Wen,

Zhang

et al. 2023

Applied Sciences

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Ocean reverberations, a significant interference source in active sonar, arise as a response generated by random scattering at the receiving end, a consequence of randomly distributed clutter or irregular interfaces. Statistical analysis of reverberation data has revealed a predominant adherence to the Rayleigh distribution, signifying its departure from specific distribution forms like the Gaussian distribution. This study introduces the Gaussian mixture model, capable of simulating random variables conforming to a wide array of distributions through the integration of an adequate number of components. Leveraging the unique statistical attributes of reverberation, we initiate the Gaussian mixture model’s parameters via the frequency histogram of the reverberation data. Subsequently, model parameters are estimated using the expectation–maximization (EM) algorithm and the most suitable statistical model is selected based on robust model selection criteria. Through a comprehensive evaluation that encompasses both simulated and observed data, our results underscore the Gaussian mixture model’s effectiveness in accurately characterizing the distribution of reverberation data, yielding a mean squared error of less than 4‰.

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Section: Introductionmentioning

confidence: 99%