Отправить статью по E-mail Entropy Dimension Reduction Method for Randomized Machine Learning Problems
- Авторы: Popkov Y.S.1,2,3, Dubnov Y.A.1,3,4, Popkov A.Y.1,5
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Учреждения:
- 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
- Выпуск: Том 79, № 11 (2018)
- Страницы: 2038-2051
- Раздел: 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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Аннотация
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.
Об авторах
Yu. Popkov
Institute for Systems Analysis, Russian Academy of Sciences; Braude College of Haifa University; National Research University “Higher School of Economics,”
Автор, ответственный за переписку.
Email: popkov@isa.ru
Россия, 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
Россия, Moscow; Moscow; Moscow
A. Popkov
Institute for Systems Analysis, Russian Academy of Sciences; Peoples’ Friendship University
Email: popkov@isa.ru
Россия, Moscow; Moscow
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