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Avtomatika i Telemekhanika, 2018, Issue 11, Pages 106–122
DOI: https://doi.org/10.31857/S000523100002747-5 (Mi at14974) 		 
This article is cited in 5 scientific papers (total in 5 papers)

Control in Technical Systems

Entropy dimension reduction method for randomized machine learning problems

Yu. S. Popkovabc, Yu. A. Dubnovacd, A. Yu. Popkovae

a Institute for Systems Analysis, Russian Academy of Sciences, Federal Research Center “Informatics and Control”, Moscow, Russia
b Braude College of Haifa University, Carmiel, Israel
c National Research University “Higher School of Economics”, Moscow, Russia
d Moscow Institute of Physics and Technology, Moscow, Russia
e Peoples' Friendship University, Moscow, Russia
Full-text PDF (645 kB) Citations (5)
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DOI: https://doi.org/10.31857/S000523100002747-5
Abstract: The direct and inverse projections (DIP) method was proposed to reduce the feature space to the given dimensions oriented to the problems of randomized machine learning and based on the procedure of “direct” and “inverse” design. The “projector” matrices are determined by maximizing the relative entropy. It is suggested to estimate the information losses by the absolute error calculated with the use of the Kullback–Leibler function (SRC method). An example illustrating these methods was given.
Keywords: entropy, relative entropy, projection operators, matrix derivatives, gradient method, direct and inverse projections.
Funding agency Grant number
Russian Foundation for Basic Research 17-07-00286_à
17-29-03119_îôè_ì
This work was supported by the Russian Foundation for Basic Research, projects nos. 17-07-00286 and 17-29-03119.
Presented by the member of Editorial Board: P. S. Shcherbakov

Received: 24.01.2018
English version:
Automation and Remote Control, 2018, Volume 79, Issue 11, Pages 2038–2051
DOI: https://doi.org/10.1134/S0005117918110085
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: Yu. S. Popkov, Yu. A. Dubnov, A. Yu. Popkov, “Entropy dimension reduction method for randomized machine learning problems”, Avtomat. i Telemekh., 2018, no. 11, 106–122; Autom. Remote Control, 79:11 (2018), 2038–2051
Citation in format AMSBIB
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    • This publication is cited in the following 5 articles:
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