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Avtomatika i Telemekhanika, 2019, Issue 8, Pages 44–63
DOI: https://doi.org/10.1134/S0005231019080051
(Mi at15315)
 

This article is cited in 4 scientific papers (total in 4 papers)

Stochastic Systems

Stochastic approximation algorithm with randomization at the input for unsupervised parameters estimation of Gaussian mixture model with sparse parameters

A. A. Boyarovab, O. N. Granichinab

a St. Petersburg State University, St. Petersburg, Russia
b Institute for Problems of Mechanical Engineering, Russian Academy of Sciences, St. Petersburg, Russia
References:
Abstract: We consider the possibilities of using stochastic approximation algorithms with randomization on the input under unknown but bounded interference in studying the clustering of data generated by a mixture of Gaussian distributions. The proposed algorithm, which is robust to external disturbances, allows us to process the data “on the fly” and has a high convergence rate. The operation of the algorithm is illustrated by examples of its use for clustering in various difficult conditions.
Keywords: clustering, unsupervised learning, randomization, stochastic approximation, Gaussian mixture model.
Funding agency Grant number
Russian Science Foundation 16-19-00057
This work was partially supported by the Russian Science Foundation, project no. 16-19-00057.

Received: 01.06.2017
Revised: 19.12.2018
Accepted: 07.02.2019
English version:
Automation and Remote Control, 2019, Volume 80, Issue 8, Pages 1403–1418
DOI: https://doi.org/10.1134/S0005117919080034
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: A. A. Boyarov, O. N. Granichin, “Stochastic approximation algorithm with randomization at the input for unsupervised parameters estimation of Gaussian mixture model with sparse parameters”, Avtomat. i Telemekh., 2019, no. 8, 44–63; Autom. Remote Control, 80:8 (2019), 1403–1418
Citation in format AMSBIB
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\by A.~A.~Boyarov, O.~N.~Granichin
\paper Stochastic approximation algorithm with randomization at the input for unsupervised parameters estimation of Gaussian mixture model with sparse parameters
\jour Avtomat. i Telemekh.
\yr 2019
\issue 8
\pages 44--63
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\crossref{https://doi.org/10.1134/S0005231019080051}
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\transl
\jour Autom. Remote Control
\yr 2019
\vol 80
\issue 8
\pages 1403--1418
\crossref{https://doi.org/10.1134/S0005117919080034}
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  • https://www.mathnet.ru/eng/at/y2019/i8/p44
  • This publication is cited in the following 4 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
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