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Simultaneous Perturbation Stochastic Approximation for Clustering of a Gaussian Mixture Model under Unknown but Bounded Disturbances. / Boiarov, Andrei; Granichin, Oleg; Hou Wenguang.

2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017). IEEE Canada, 2017. p. 1740-1745.

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

Harvard

Boiarov, A, Granichin, O & Hou Wenguang 2017, Simultaneous Perturbation Stochastic Approximation for Clustering of a Gaussian Mixture Model under Unknown but Bounded Disturbances. in 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017). IEEE Canada, pp. 1740-1745, 1st Annual IEEE Conference on Control Technology and Applications, United States, 27/08/17.

APA

Boiarov, A., Granichin, O., & Hou Wenguang (2017). Simultaneous Perturbation Stochastic Approximation for Clustering of a Gaussian Mixture Model under Unknown but Bounded Disturbances. In 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017) (pp. 1740-1745). IEEE Canada.

Vancouver

Boiarov A, Granichin O, Hou Wenguang. Simultaneous Perturbation Stochastic Approximation for Clustering of a Gaussian Mixture Model under Unknown but Bounded Disturbances. In 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017). IEEE Canada. 2017. p. 1740-1745

Author

Boiarov, Andrei ; Granichin, Oleg ; Hou Wenguang. / Simultaneous Perturbation Stochastic Approximation for Clustering of a Gaussian Mixture Model under Unknown but Bounded Disturbances. 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017). IEEE Canada, 2017. pp. 1740-1745

BibTeX

@inproceedings{7e8d1b4d0698482186006cb94e0315b4,
title = "Simultaneous Perturbation Stochastic Approximation for Clustering of a Gaussian Mixture Model under Unknown but Bounded Disturbances",
abstract = "Multidimensional optimization holds a central role in many machine learning problems. When a model quality functional is measured with an almost arbitrary external noise, it makes sense to use randomized optimization techniques. This paper deals with the problem of clustering of a Gaussian mixture model under unknown but bounded disturbances. We introduce a stochastic approximation algorithm with randomly perturbed input (like SPSA) to solve this problem. The proposed method is appropriate for the online learning with streaming data, and it has a high speed of convergence. We study the conditions of the SPSA clustering algorithm applicability and show illustrative examples.",
keywords = "Clustering, Gaussian mixture model, randomized algorithm, SPSA, unknown but bounded disturbances, ALGORITHM, INPUT",
author = "Andrei Boiarov and Oleg Granichin and {Hou Wenguang}",
year = "2017",
language = "Английский",
pages = "1740--1745",
booktitle = "2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017)",
publisher = "IEEE Canada",
address = "Канада",
note = "null ; Conference date: 27-08-2017 Through 30-08-2017",

}

RIS

TY - GEN

T1 - Simultaneous Perturbation Stochastic Approximation for Clustering of a Gaussian Mixture Model under Unknown but Bounded Disturbances

AU - Boiarov, Andrei

AU - Granichin, Oleg

AU - Hou Wenguang, null

PY - 2017

Y1 - 2017

N2 - Multidimensional optimization holds a central role in many machine learning problems. When a model quality functional is measured with an almost arbitrary external noise, it makes sense to use randomized optimization techniques. This paper deals with the problem of clustering of a Gaussian mixture model under unknown but bounded disturbances. We introduce a stochastic approximation algorithm with randomly perturbed input (like SPSA) to solve this problem. The proposed method is appropriate for the online learning with streaming data, and it has a high speed of convergence. We study the conditions of the SPSA clustering algorithm applicability and show illustrative examples.

AB - Multidimensional optimization holds a central role in many machine learning problems. When a model quality functional is measured with an almost arbitrary external noise, it makes sense to use randomized optimization techniques. This paper deals with the problem of clustering of a Gaussian mixture model under unknown but bounded disturbances. We introduce a stochastic approximation algorithm with randomly perturbed input (like SPSA) to solve this problem. The proposed method is appropriate for the online learning with streaming data, and it has a high speed of convergence. We study the conditions of the SPSA clustering algorithm applicability and show illustrative examples.

KW - Clustering

KW - Gaussian mixture model

KW - randomized algorithm

KW - SPSA

KW - unknown but bounded disturbances

KW - ALGORITHM

KW - INPUT

M3 - статья в сборнике материалов конференции

SP - 1740

EP - 1745

BT - 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017)

PB - IEEE Canada

Y2 - 27 August 2017 through 30 August 2017

ER -

ID: 32479432