Standard

Stochastic Approximation Algorithm with Randomization at the Input for Unsupervised Parameters Estimation of Gaussian Mixture Model with Sparse Parameters. / Boiarov, A. A.; Granichin, O. N.

в: Automation and Remote Control, Том 80, № 8, 01.08.2019, стр. 1403-1418.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Boiarov, AA & Granichin, ON 2019, 'Stochastic Approximation Algorithm with Randomization at the Input for Unsupervised Parameters Estimation of Gaussian Mixture Model with Sparse Parameters', Automation and Remote Control, Том. 80, № 8, стр. 1403-1418. https://doi.org/10.1134/S0005117919080034

APA

Boiarov, A. A., & Granichin, O. N. (2019). Stochastic Approximation Algorithm with Randomization at the Input for Unsupervised Parameters Estimation of Gaussian Mixture Model with Sparse Parameters. Automation and Remote Control, 80(8), 1403-1418. https://doi.org/10.1134/S0005117919080034

Vancouver

Boiarov AA, Granichin ON. Stochastic Approximation Algorithm with Randomization at the Input for Unsupervised Parameters Estimation of Gaussian Mixture Model with Sparse Parameters. Automation and Remote Control. 2019 Авг. 1;80(8):1403-1418. https://doi.org/10.1134/S0005117919080034

Author

Boiarov, A. A. ; Granichin, O. N. / Stochastic Approximation Algorithm with Randomization at the Input for Unsupervised Parameters Estimation of Gaussian Mixture Model with Sparse Parameters. в: Automation and Remote Control. 2019 ; Том 80, № 8. стр. 1403-1418.

BibTeX

@article{d2cb5ea97c8347469263bba9090c60d0,
title = "Stochastic Approximation Algorithm with Randomization at the Input for Unsupervised Parameters Estimation of Gaussian Mixture Model with Sparse Parameters",
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, Gaussian mixture model, randomization, stochastic approximation, unsupervised learning",
author = "Boiarov, {A. A.} and Granichin, {O. N.}",
year = "2019",
month = aug,
day = "1",
doi = "10.1134/S0005117919080034",
language = "Английский",
volume = "80",
pages = "1403--1418",
journal = "Automation and Remote Control",
issn = "0005-1179",
publisher = "МАИК {"}Наука/Интерпериодика{"}",
number = "8",

}

RIS

TY - JOUR

T1 - Stochastic Approximation Algorithm with Randomization at the Input for Unsupervised Parameters Estimation of Gaussian Mixture Model with Sparse Parameters

AU - Boiarov, A. A.

AU - Granichin, O. N.

PY - 2019/8/1

Y1 - 2019/8/1

N2 - 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.

AB - 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.

KW - clustering

KW - Gaussian mixture model

KW - randomization

KW - stochastic approximation

KW - unsupervised learning

UR - http://www.scopus.com/inward/record.url?scp=85070750657&partnerID=8YFLogxK

UR - http://www.mendeley.com/research/stochastic-approximation-algorithm-randomization-input-unsupervised-parameters-estimation-gaussian-m

U2 - 10.1134/S0005117919080034

DO - 10.1134/S0005117919080034

M3 - статья

AN - SCOPUS:85070750657

VL - 80

SP - 1403

EP - 1418

JO - Automation and Remote Control

JF - Automation and Remote Control

SN - 0005-1179

IS - 8

ER -

ID: 46020243