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

被引：4

作者：

Boiarov, A. A. ^{[1
,2
]}

Granichin, O. N. ^{[1
,2
]}

机构：

[1] St Petersburg State Univ, St Petersburg, Russia

[2] Russian Acad Sci, Inst Problems Mech Engn, St Petersburg, Russia

来源：

AUTOMATION AND REMOTE CONTROL | 2019年 / 80卷 / 08期

基金：

俄罗斯科学基金会;

关键词：

clustering; unsupervised learning; randomization; stochastic approximation; Gaussian mixture model;

D O I：

10.1134/S0005117919080034

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1403 / 1418

页数：16

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