NDLI: Dynamically regularized harmony learning of Gaussian mixtures

Content Provider	IEEE Xplore Digital Library
Author	Hongyan Wang Jinwen Ma
Copyright Year	2014
Description	Author affiliation: Dept. of Inf. Sci., Peking Univ., Beijing, China (Hongyan Wang; Jinwen Ma)
Abstract	In this paper, a dynamically regularized harmony learning (DRHL) algorithm is proposed for Gaussian mixture learning with a favourite feature of both adaptive model selection and consistent parameter estimation. Specifically, under the framework of Bayesian Ying-Yang (BYY) harmony learning, we utilize the average Shannon entropy of the posterior probability per sample as a regularization term being controlled by a scale factor to the harmony function on Gaussian mixtures increasing from 0 to 1 dynamically. It is demonstrated by the experiments on both synthetic and real-world datasets that the DRHL algorithm can not only select the correct number of actual Gaussians in the dataset, but also obtain the maximum likelihood (ML) estimators of the parameters in the actual mixture. Moreover, the DRHL algorithm is scalable and can be implemented on a big dataset.
Starting Page	1158
Ending Page	1164
File Size	287748
Page Count	7
File Format	PDF
ISBN	9781479938407
DOI	10.1109/SMC.2014.6974070
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Heuristic algorithms Maximum likelihood estimation Bayes methods Parameter estimation Entropy Vectors maximum likelihood Gaussian mixtures model selection regularization
Content Type	Text
Resource Type	Article

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