Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 287, Issue 1, Pages 19-35Publisher
ELSEVIER
DOI: 10.1016/j.ejor.2020.04.032
Keywords
Data mining; Nonsmooth optimization; Clusterwise linear regression; DC optimization; Bundle methods
Funding
- Matti Programme of the University of Turku Graduate School UTUGS
- University of Turku
- Academy of Finland [289500, 294002, 319274]
- Australian Government through the Australian Research Council [DP190100580]
- Academy of Finland (AKA) [294002, 319274, 319274, 294002] Funding Source: Academy of Finland (AKA)
Ask authors/readers for more resources
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. (C) 2020 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available