A Two-Stage Method for Constructing Linear Regressions
Using Optimal Convex Combinations
O. V. Senkoa, A. A. Dokukina,*, N. N. Kiselyovab, and N. Yu. Khomutova
Translated by I. Ruzanova
a Dorodnicyn Computing Center, Federal Research Center “Computer Science and Control”, Russian Academy
of Sciences, Moscow, 119333 Russia
b Baikov Institute of Metallurgy and Materials Science, Russian Academy of Sciences, Moscow, 119991 Russia
Correspondence to: *e-mail: dalex@ccas.ru
Received 10 November, 2017
Abstract—Multilevel learning systems have become more popular in pattern recognition and regression analysis. In this paper, a two-level method for constructing a multidimensional regression model is considered, in which a family of optimal convex combinations of simple one-dimensional least-square regressions is generated at the first level. The second level of the proposed learning system is given by an elastic net. Experimental verification presented demonstrate the efficiency of the proposed regression estimation method as applied to problems with a small amount of data.
DOI: 10.1134/S1064562418020035