A Comparative Study on Lagrange Ying-Yang Alternation Method in Gaussian Mixture-Based Clustering
Refereed conference paper presented and published in conference proceedings

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AbstractGaussian Mixture Model (GMM) has been applied to clustering with wide applications in image segmentation, object detection and so on. Many algorithms were proposed to learn GMM with appropriate number of Gaussian components automatically determined. Lagrange Ying-Yang alternation method (LYYA) is one of them and it has advantages of no priors as well as the posterior probability bounded by traditional probability space. This paper aims to investigate the performance of LYYA, in comparisons with other methods including Bayesian Ying-Yang (BYY) learning, Rival penalized competitive learning (RPCL), hard-cut Expectation Maximization (EM) method, and classic EM with Bayesian Information Criterion (BIC). Systematic simulations show that LYYA is generally more robust than others on the data generated by varying sample size, data dimensionality and real components number. Unsupervised image segmentation results on Berkeley datasets also confirm LYYA advantages when comparing to the Mean shift and Multiscale graph decomposition algorithms.
Acceptance Date06/10/2017
All Author(s) ListWeijian Long, Shikui Tu, Lei Xu
Name of Conference18th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
Start Date of Conference30/10/2017
End Date of Conference01/11/2017
Place of ConferenceGuilin
Country/Region of ConferenceChina
Proceedings TitleIntelligent Data Engineering and Automated Learning – IDEAL 2017
Series TitleLecture Notes in Computer Science
Number in SeriesLNCS 10585
Volume Number10585
Pages489 - 499
LanguagesEnglish-United States
KeywordsGaussian Mixture Model, Lagrange Ying-Yang alternation method, Unsupervised image segmentation, Lagrange coefficient

Last updated on 2021-27-09 at 05:20