The Effect of Uniform Data Quantization on GMM-based Clustering by Means of EM Algorithm

Jovanović, Aleksandra; Perić, Zoran; Nikolić, Jelena; Aleksić, Danijela

doi:10.1109/infoteh51037.2021.9400662

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of parameter estimation using the EM algorithm, it is crucial to predefine the number of clusters (K), means, and variances. Without proper initialization of these parameters, the EM algorithm is susceptible to converging towards local optima or even experiencing convergence failures [21]. In this study, we adopt a systematic approach to address this issue.…”

Section: Improved Em Parameter Estimation Methodsmentioning

confidence: 99%

Gaussian Mixture Model for Marine Reverberations

Sun,

Wen,

Zhang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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‰.

show abstract

Section: Improved Em Parameter Estimation Methodsmentioning

confidence: 99%