Entropy Dimension Reduction Method for Randomized Machine Learning Problems
- Authors: Popkov Y.S.1,2,3, Dubnov Y.A.1,3,4, Popkov A.Y.1,5
- 
							Affiliations: 
							- 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
 
- Issue: Vol 79, No 11 (2018)
- Pages: 2038-2051
- Section: Control in Technical Systems
- URL: https://journals.rcsi.science/0005-1179/article/view/151074
- DOI: https://doi.org/10.1134/S0005117918110085
- ID: 151074
Cite item
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.
About the authors
Yu. S. Popkov
Institute for Systems Analysis, Russian Academy of Sciences; Braude College of Haifa University; National Research University “Higher School of Economics,”
							Author for correspondence.
							Email: popkov@isa.ru
				                					                																			                												                	Russian Federation, 							Moscow; Carmiel; Moscow						
Yu. A. 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
				                					                																			                												                	Russian Federation, 							Moscow; Moscow; Moscow						
A. Yu. Popkov
Institute for Systems Analysis, Russian Academy of Sciences; Peoples’ Friendship University
														Email: popkov@isa.ru
				                					                																			                												                	Russian Federation, 							Moscow; Moscow						
Supplementary files
 
				
			 
					 
						 
						 
						 
						 
				 
  
  
  
  
  Email this article
			Email this article  Open Access
		                                Open Access Access granted
						Access granted Subscription Access
		                                		                                        Subscription Access
		                                					