Enviar artigo por via de e-mail Entropy Dimension Reduction Method for Randomized Machine Learning Problems
- Autores: Popkov Y.S.1,2,3, Dubnov Y.A.1,3,4, Popkov A.Y.1,5
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Afiliações:
- Institute for Systems Analysis, Russian Academy of Sciences
- Braude College of Haifa University
- National Research University “Higher School of Economics,”
- Moscow Institute of Physics and Technology
- Peoples’ Friendship University
- Edição: Volume 79, Nº 11 (2018)
- Páginas: 2038-2051
- Seção: Control in Technical Systems
- URL: https://journals.rcsi.science/0005-1179/article/view/151074
- DOI: https://doi.org/10.1134/S0005117918110085
- ID: 151074
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Resumo
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.
Sobre autores
Yu. Popkov
Institute for Systems Analysis, Russian Academy of Sciences; Braude College of Haifa University; National Research University “Higher School of Economics,”
Autor responsável pela correspondência
Email: popkov@isa.ru
Rússia, Moscow; Carmiel; Moscow
Yu. Dubnov
Institute for Systems Analysis, Russian Academy of Sciences; National Research University “Higher School of Economics,”; Moscow Institute of Physics and Technology
Email: popkov@isa.ru
Rússia, Moscow; Moscow; Moscow
A. Popkov
Institute for Systems Analysis, Russian Academy of Sciences; Peoples’ Friendship University
Email: popkov@isa.ru
Rússia, Moscow; Moscow
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