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Comparative study for multi-variable regression methods based on Laguerre polynomial and manifolds optimization

Zibo Li (Faculty of Information Technology, Beijing University of Technology, Beijing, China) (The Classified Information Carrier Safety Management Engineering Technology Research Center of Beijing, Beijing, China) (Beijing JingHang Research Institute of Computation and Communication, Beijing, China)
Zhengxiang Yan (Faculty of Information Technology, Beijing University of Technology, Beijing, China)
Shicheng Li (The Classified Information Carrier Safety Management Engineering Technology Research Center of Beijing, Beijing, China) (Beijing JingHang Research Institute of Computation and Communication, Beijing, China)
Guangmin Sun (Faculty of Information Technology, Beijing University of Technology, Beijing, China)
Xin Wang (The Classified Information Carrier Safety Management Engineering Technology Research Center of Beijing, Beijing, China) (Beijing JingHang Research Institute of Computation and Communication, Beijing, China)
Dequn Zhao (Faculty of Information Technology, Beijing University of Technology, Beijing, China)
Yu Li (Faculty of Information Technology, Beijing University of Technology, Beijing, China)
Xiucheng Liu (College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China)

Engineering Computations

ISSN: 0264-4401

Article publication date: 16 August 2022

Issue publication date: 23 August 2022

67

Abstract

Purpose

The purpose of this paper is to overcome the application limitations of other multi-variable regression based on polynomials due to the huge computation room and time cost.

Design/methodology/approach

In this paper, based on the idea of feature selection and cascaded regression, two strategies including Laguerre polynomials and manifolds optimization are proposed to enhance the accuracy of multi-variable regression. Laguerre polynomials were combined with the genetic algorithm to enhance the capacity of polynomials approximation and the manifolds optimization method was introduced to solve the co-related optimization problem.

Findings

Two multi-variable Laguerre polynomials regression methods are designed. Firstly, Laguerre polynomials are combined with feature selection method. Secondly, manifolds component analysis is adopted in cascaded Laguerre polynomials regression method. Two methods are brought to enhance the accuracy of multi-variable regression method.

Research limitations/implications

With the increasing number of variables in regression problem, the stable accuracy performance might not be kept by using manifold-based optimization method. Moreover, the methods mentioned in this paper are not suitable for the classification problem.

Originality/value

Experiments are conducted on three types of datasets to evaluate the performance of the proposed regression methods. The best accuracy was achieved by the combination of cascade, manifold optimization and Chebyshev polynomials, which implies that the manifolds optimization has stronger contribution than the genetic algorithm and Laguerre polynomials.

Keywords

Acknowledgements

This work was supported by the National Key R&D Program of China [2018YFF01012300] and the National Natural Science Foundation of China [11527801, 41706201], and the Strategic Priority Program of the Chinese Academy of Sciences (XDB41000000).

Citation

Li, Z., Yan, Z., Li, S., Sun, G., Wang, X., Zhao, D., Li, Y. and Liu, X. (2022), "Comparative study for multi-variable regression methods based on Laguerre polynomial and manifolds optimization", Engineering Computations, Vol. 39 No. 8, pp. 3058-3082. https://doi.org/10.1108/EC-12-2021-0766

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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