- Title
- Clusterwise support vector linear regression
- Creator
- Joki, Kaisa; Bagirov, Adil; Karmitsa, Napsu; Mäkelä, Marko; Taheri, Sona
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/173348
- Identifier
- vital:14655
- Identifier
-
https://doi.org/10.1016/j.ejor.2020.04.032
- Identifier
- ISBN:0377-2217 (ISSN)
- Abstract
- In clusterwise linear regression (CLR), the aim is to simultaneously partition data into a given number of clusters and to find regression coefficients for each cluster. In this paper, we propose a novel approach to model and solve the CLR problem. The main idea is to utilize the support vector machine (SVM) approach to model the CLR problem by using the SVM for regression to approximate each cluster. This new formulation of the CLR problem is represented as an unconstrained nonsmooth optimization problem, where we minimize a difference of two convex (DC) functions. To solve this problem, a method based on the combination of the incremental algorithm and the double bundle method for DC optimization is designed. Numerical experiments are performed to validate the reliability of the new formulation for CLR and the efficiency of the proposed method. The results show that the SVM approach is suitable for solving CLR problems, especially, when there are outliers in data. © 2020 Elsevier B.V.; Funding details: Academy of Finland, 289500, 294002, 319274 Funding details: Turun Yliopisto Funding details: Australian Research Council, ARC, (Project no. DP190100580 ).
- Publisher
- Elsevier B.V.
- Relation
- European Journal of Operational Research Vol. 287, no. 1 (2020), p. 19-35
- Rights
- Copyright © 2020 Elsevier B.V. All rights reserved.
- Rights
- This metadata is freely available under a CCO license
- Rights
- Open Access
- Subject
- MD Multidisciplinary; Bundle methods; Clusterwise linear regression; Data mining; DC optimization; Nonsmooth optimization
- Full Text
- Reviewed
- Funder
- Funding details: Academy of Finland, 289500, 294002, 319274 Funding details: Turun Yliopisto Funding details: Australian Research Council, ARC, (Project no. DP190100580 ).
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