- Title
- Incremental DC optimization algorithm for large-scale clusterwise linear regression
- Creator
- Bagirov, Adil; Taheri, Sona; Cimen, Emre
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/175473
- Identifier
- vital:14992
- Identifier
-
https://doi.org10.1016/j.cam.2020.113323
- Identifier
- ISBN:0377-0427
- Abstract
- The objective function in the nonsmooth optimization model of the clusterwise linear regression (CLR) problem with the squared regression error is represented as a difference of two convex functions. Then using the difference of convex algorithm (DCA) approach the CLR problem is replaced by the sequence of smooth unconstrained optimization subproblems. A new algorithm based on the DCA and the incremental approach is designed to solve the CLR problem. We apply the Quasi-Newton method to solve the subproblems. The proposed algorithm is evaluated using several synthetic and real-world data sets for regression and compared with other algorithms for CLR. Results demonstrate that the DCA based algorithm is efficient for solving CLR problems with the large number of data points and in particular, outperforms other algorithms when the number of input variables is small. © 2020 Elsevier B.V.
- Publisher
- Elsevier B.V.
- Relation
- Journal of Computational and Applied Mathematics Vol. 389, no. (2021), p. 1-17; https://purl.org/au-research/grants/arc/DP190100580
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright Elsevier
- Subject
- Clusterwise linear regression; DC optimization; Nonconvex optimization; Nonsmooth optimization; Regression analysis; Functions; Newton-Raphson method; Clusterwise linear regressions; Difference of convex algorithms; Incremental approach; Objective functions; Quasi-Newton methods; Regression errors; Unconstrained optimization; Optimization; 0102 Applied Mathematics; 0103 Numerical and Computational Mathematics; 0906 Electrical and Electronic Engineering
- Reviewed
- Funder
- Australian Research Council, ARC, DP190100580 and Dr. E. Cimen was supported by Anadolu University Scientific Research Projects Commission, Turkey under the Grant No. 1506F499
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