Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering

Yang Ni; David Jones; Zeya Wang

doi:10.1109/BigData50022.2020.9378319

Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering

Yang Ni, David Jones, Zeya Wang

Dr. Bing Zhang Department of Statistics

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

5 Scopus citations

Abstract

We propose both the first embarrassingly parallel consensus variational inference algorithm and a new consensus Monte Carlo algorithm for efficient implementation of Bayesian nonparametric mixture models. The proposed algorithms are based on a group clustering approach, and they substantially accelerate inference and reduce memory costs compared with standard Markov chain Monte Carlo and variational inference algorithms for clustering. We demonstrate that our proposed algorithms are significantly faster than competing methods while maintaining the same clustering accuracy. Due to their simplicity and embarrassingly parallel nature, our proposed algorithms are straightforward to implement and widely applicable beyond the models and applications considered in this paper.

Original language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
Editors	Xintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
Pages	204-209
Number of pages	6
ISBN (Electronic)	9781728162515
DOIs	https://doi.org/10.1109/BigData50022.2020.9378319
State	Published - Dec 10 2020
Event	8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States Duration: Dec 10 2020 → Dec 13 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference	8th IEEE International Conference on Big Data, Big Data 2020
Country/Territory	United States
City	Virtual, Atlanta
Period	12/10/20 → 12/13/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

big data
embarrassingly parallel computing
image clustering
unsupervised learning

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality

Access to Document

10.1109/BigData50022.2020.9378319

Cite this

Ni, Y., Jones, D., & Wang, Z. (2020). Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering. In X. Wu, C. Jermaine, L. Xiong, X. T. Hu, O. Kotevska, S. Lu, W. Xu, S. Aluru, C. Zhai, E. Al-Masri, Z. Chen, & J. Saltz (Eds.), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp. 204-209). Article 9378319 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). https://doi.org/10.1109/BigData50022.2020.9378319

Ni, Yang ; Jones, David ; Wang, Zeya. / Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering. Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. editor / Xintao Wu ; Chris Jermaine ; Li Xiong ; Xiaohua Tony Hu ; Olivera Kotevska ; Siyuan Lu ; Weijia Xu ; Srinivas Aluru ; Chengxiang Zhai ; Eyhab Al-Masri ; Zhiyuan Chen ; Jeff Saltz. 2020. pp. 204-209 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

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abstract = "We propose both the first embarrassingly parallel consensus variational inference algorithm and a new consensus Monte Carlo algorithm for efficient implementation of Bayesian nonparametric mixture models. The proposed algorithms are based on a group clustering approach, and they substantially accelerate inference and reduce memory costs compared with standard Markov chain Monte Carlo and variational inference algorithms for clustering. We demonstrate that our proposed algorithms are significantly faster than competing methods while maintaining the same clustering accuracy. Due to their simplicity and embarrassingly parallel nature, our proposed algorithms are straightforward to implement and widely applicable beyond the models and applications considered in this paper.",

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author = "Yang Ni and David Jones and Zeya Wang",

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year = "2020",

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doi = "10.1109/BigData50022.2020.9378319",

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Ni, Y, Jones, D & Wang, Z 2020, Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering. in X Wu, C Jermaine, L Xiong, XT Hu, O Kotevska, S Lu, W Xu, S Aluru, C Zhai, E Al-Masri, Z Chen & J Saltz (eds), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020., 9378319, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, pp. 204-209, 8th IEEE International Conference on Big Data, Big Data 2020, Virtual, Atlanta, United States, 12/10/20. https://doi.org/10.1109/BigData50022.2020.9378319

Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering. / Ni, Yang; Jones, David; Wang, Zeya.
Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. ed. / Xintao Wu; Chris Jermaine; Li Xiong; Xiaohua Tony Hu; Olivera Kotevska; Siyuan Lu; Weijia Xu; Srinivas Aluru; Chengxiang Zhai; Eyhab Al-Masri; Zhiyuan Chen; Jeff Saltz. 2020. p. 204-209 9378319 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

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

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N2 - We propose both the first embarrassingly parallel consensus variational inference algorithm and a new consensus Monte Carlo algorithm for efficient implementation of Bayesian nonparametric mixture models. The proposed algorithms are based on a group clustering approach, and they substantially accelerate inference and reduce memory costs compared with standard Markov chain Monte Carlo and variational inference algorithms for clustering. We demonstrate that our proposed algorithms are significantly faster than competing methods while maintaining the same clustering accuracy. Due to their simplicity and embarrassingly parallel nature, our proposed algorithms are straightforward to implement and widely applicable beyond the models and applications considered in this paper.

AB - We propose both the first embarrassingly parallel consensus variational inference algorithm and a new consensus Monte Carlo algorithm for efficient implementation of Bayesian nonparametric mixture models. The proposed algorithms are based on a group clustering approach, and they substantially accelerate inference and reduce memory costs compared with standard Markov chain Monte Carlo and variational inference algorithms for clustering. We demonstrate that our proposed algorithms are significantly faster than competing methods while maintaining the same clustering accuracy. Due to their simplicity and embarrassingly parallel nature, our proposed algorithms are straightforward to implement and widely applicable beyond the models and applications considered in this paper.

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Ni Y, Jones D, Wang Z. Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering. In Wu X, Jermaine C, Xiong L, Hu XT, Kotevska O, Lu S, Xu W, Aluru S, Zhai C, Al-Masri E, Chen Z, Saltz J, editors, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. 2020. p. 204-209. 9378319. (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). doi: 10.1109/BigData50022.2020.9378319

Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

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