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Sparse Gaussian mixture model clustering via simultaneous perturbation stochastic approximation. / Boiarov, Andrei; Granichin, Oleg.

в: IFAC-PapersOnLine, Том 53, № 2, 2020, стр. 995-1000.

Результаты исследований: Научные публикации в периодических изданиях › статья в журнале по материалам конференции › Рецензирование

Harvard

Boiarov, A & Granichin, O 2020, 'Sparse Gaussian mixture model clustering via simultaneous perturbation stochastic approximation', IFAC-PapersOnLine, Том. 53, № 2, стр. 995-1000. https://doi.org/10.1016/j.ifacol.2020.12.1276

APA

Boiarov, A., & Granichin, O. (2020). Sparse Gaussian mixture model clustering via simultaneous perturbation stochastic approximation. IFAC-PapersOnLine, 53(2), 995-1000. https://doi.org/10.1016/j.ifacol.2020.12.1276

Vancouver

Boiarov A, Granichin O. Sparse Gaussian mixture model clustering via simultaneous perturbation stochastic approximation. IFAC-PapersOnLine. 2020;53(2):995-1000. https://doi.org/10.1016/j.ifacol.2020.12.1276

Author

Boiarov, Andrei ; Granichin, Oleg. / Sparse Gaussian mixture model clustering via simultaneous perturbation stochastic approximation. в: IFAC-PapersOnLine. 2020 ; Том 53, № 2. стр. 995-1000.

BibTeX

@article{1dee2dfd4b824ffe84a9bd474b4f70e1,
title = "Sparse Gaussian mixture model clustering via simultaneous perturbation stochastic approximation",
abstract = "In this paper the problem of a multidimensional optimization in unsupervised learning and clustering is studied under significant uncertainties in the data model and measurements of penalty functions. We propose a modified version of SPSA-based algorithm which maintains stability under conditions such as a sparse Gaussian mixture model. This data model is important because it can be effectively used to evaluate the noise model in many practical systems. The proposed algorithm is robust to external disturbances and is able to process data sequentially, “on the fly”. In this paper provides a study of this algorithm and its mathematical justification. The behavior of the algorithm is illustrated by examples of its use for clustering in various difficult conditions.",
keywords = "Gaussian distributions, Learning algorithms, Machine learning, Optimization, Stochastic approximation",
author = "Andrei Boiarov and Oleg Granichin",
note = "Publisher Copyright: Copyright {\textcopyright} 2020 The Authors. This is an open access article under the CC BY-NC-ND license Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 21st IFAC World Congress 2020 ; Conference date: 12-07-2020 Through 17-07-2020",
year = "2020",
doi = "10.1016/j.ifacol.2020.12.1276",
language = "English",
volume = "53",
pages = "995--1000",
journal = "IFAC-PapersOnLine",
issn = "2405-8971",
publisher = "Elsevier",
number = "2",

}

RIS

TY - JOUR

T1 - Sparse Gaussian mixture model clustering via simultaneous perturbation stochastic approximation

AU - Boiarov, Andrei

AU - Granichin, Oleg

N1 - Publisher Copyright: Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - In this paper the problem of a multidimensional optimization in unsupervised learning and clustering is studied under significant uncertainties in the data model and measurements of penalty functions. We propose a modified version of SPSA-based algorithm which maintains stability under conditions such as a sparse Gaussian mixture model. This data model is important because it can be effectively used to evaluate the noise model in many practical systems. The proposed algorithm is robust to external disturbances and is able to process data sequentially, “on the fly”. In this paper provides a study of this algorithm and its mathematical justification. The behavior of the algorithm is illustrated by examples of its use for clustering in various difficult conditions.

AB - In this paper the problem of a multidimensional optimization in unsupervised learning and clustering is studied under significant uncertainties in the data model and measurements of penalty functions. We propose a modified version of SPSA-based algorithm which maintains stability under conditions such as a sparse Gaussian mixture model. This data model is important because it can be effectively used to evaluate the noise model in many practical systems. The proposed algorithm is robust to external disturbances and is able to process data sequentially, “on the fly”. In this paper provides a study of this algorithm and its mathematical justification. The behavior of the algorithm is illustrated by examples of its use for clustering in various difficult conditions.

KW - Gaussian distributions

KW - Learning algorithms

KW - Machine learning

KW - Optimization

KW - Stochastic approximation

UR - http://www.scopus.com/inward/record.url?scp=85105088514&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2020.12.1276

DO - 10.1016/j.ifacol.2020.12.1276

M3 - Conference article

AN - SCOPUS:85105088514

VL - 53

SP - 995

EP - 1000

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8971

IS - 2

T2 - 21st IFAC World Congress 2020

Y2 - 12 July 2020 through 17 July 2020

ER -

ID: 78863775