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
收藏
页码：1403 / 1418
页数：16
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