Computational efficient Variational Bayesian Gaussian Mixture Models via Coreset

Min Zhang; Yinlin Fu; Kevin M. Bennett; Teresa Wu

doi:10.1109/CITS.2016.7546405

Computational efficient Variational Bayesian Gaussian Mixture Models via Coreset

Min Zhang, Yinlin Fu, Kevin M. Bennett, Teresa Wu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Variational Bayesian Gaussian Mixture Model is a popular clustering algorithm with a reliable performance. However, it is noted that the model fitting process takes long time, especially when dealing with large scale data, since it utilizes the whole dataset. To address this issue, in paper we propose a new algorithm termed a weighted VBGMM via Coreset. Specifically, a new coreset construction method is first proposed to sample the data which is used to fit the model. To evaluate the algorithm, two datasets are used: 1) six rat kidney images datasets 2) three human kidney images datasets. The results show that our proposed algorithm is much faster (∼ 20 times) comparing to classic VBGMM while maintaining the similar performance on whole dataset.

Original language	English (US)
Title of host publication	IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems
Editors	Fei Gao, Zan Li, Daniel Cascado Caballero, Jing Fan, Mohammad S. Obaidat, Petros Nicoploitidis, Kuei Fang Hsiao
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509034406
DOIs	https://doi.org/10.1109/CITS.2016.7546405
State	Published - Aug 16 2016
Event	2016 International Conference on Computer, Information and Telecommunication Systems, CITS 2016 - Kunming, China Duration: Jul 6 2016 → Jul 8 2016

Publication series

Name	IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems

Other

Other	2016 International Conference on Computer, Information and Telecommunication Systems, CITS 2016
Country/Territory	China
City	Kunming
Period	7/6/16 → 7/8/16

Keywords

Variational Bayesian Gaussian Mixture Model (VBGMM)
coreset

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Information Systems

Access to Document

10.1109/CITS.2016.7546405

Cite this

Zhang, M., Fu, Y., Bennett, K. M., & Wu, T. (2016). Computational efficient Variational Bayesian Gaussian Mixture Models via Coreset. In F. Gao, Z. Li, D. C. Caballero, J. Fan, M. S. Obaidat, P. Nicoploitidis, & K. F. Hsiao (Eds.), IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems Article 7546405 (IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CITS.2016.7546405

Computational efficient Variational Bayesian Gaussian Mixture Models via Coreset. / Zhang, Min; Fu, Yinlin; Bennett, Kevin M. et al.
IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems. ed. / Fei Gao; Zan Li; Daniel Cascado Caballero; Jing Fan; Mohammad S. Obaidat; Petros Nicoploitidis; Kuei Fang Hsiao. Institute of Electrical and Electronics Engineers Inc., 2016. 7546405 (IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, M, Fu, Y, Bennett, KM & Wu, T 2016, Computational efficient Variational Bayesian Gaussian Mixture Models via Coreset. in F Gao, Z Li, DC Caballero, J Fan, MS Obaidat, P Nicoploitidis & KF Hsiao (eds), IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems., 7546405, IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems, Institute of Electrical and Electronics Engineers Inc., 2016 International Conference on Computer, Information and Telecommunication Systems, CITS 2016, Kunming, China, 7/6/16. https://doi.org/10.1109/CITS.2016.7546405

Zhang M, Fu Y, Bennett KM, Wu T. Computational efficient Variational Bayesian Gaussian Mixture Models via Coreset. In Gao F, Li Z, Caballero DC, Fan J, Obaidat MS, Nicoploitidis P, Hsiao KF, editors, IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems. Institute of Electrical and Electronics Engineers Inc. 2016. 7546405. (IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems). doi: 10.1109/CITS.2016.7546405

Zhang, Min ; Fu, Yinlin ; Bennett, Kevin M. et al. / Computational efficient Variational Bayesian Gaussian Mixture Models via Coreset. IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems. editor / Fei Gao ; Zan Li ; Daniel Cascado Caballero ; Jing Fan ; Mohammad S. Obaidat ; Petros Nicoploitidis ; Kuei Fang Hsiao. Institute of Electrical and Electronics Engineers Inc., 2016. (IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems).

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abstract = "Variational Bayesian Gaussian Mixture Model is a popular clustering algorithm with a reliable performance. However, it is noted that the model fitting process takes long time, especially when dealing with large scale data, since it utilizes the whole dataset. To address this issue, in paper we propose a new algorithm termed a weighted VBGMM via Coreset. Specifically, a new coreset construction method is first proposed to sample the data which is used to fit the model. To evaluate the algorithm, two datasets are used: 1) six rat kidney images datasets 2) three human kidney images datasets. The results show that our proposed algorithm is much faster (∼ 20 times) comparing to classic VBGMM while maintaining the similar performance on whole dataset.",

keywords = "Variational Bayesian Gaussian Mixture Model (VBGMM), coreset",

author = "Min Zhang and Yinlin Fu and Bennett, {Kevin M.} and Teresa Wu",

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doi = "10.1109/CITS.2016.7546405",

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Computational efficient Variational Bayesian Gaussian Mixture Models via Coreset

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Keywords

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