- Title
- Nonsmooth nonconvex optimization approach to clusterwise linear regression problems
- Creator
- Bagirov, Adil; Ugon, Julien; Mirzayeva, Hijran
- Date
- 2013
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/67756
- Identifier
- vital:4951
- Identifier
-
https://doi.org/10.1016/j.ejor.2013.02.059
- Identifier
- ISSN:0377-2217
- Abstract
- Clusterwise regression consists of finding a number of regression functions each approximating a subset of the data. In this paper, a new approach for solving the clusterwise linear regression problems is proposed based on a nonsmooth nonconvex formulation. We present an algorithm for minimizing this nonsmooth nonconvex function. This algorithm incrementally divides the whole data set into groups which can be easily approximated by one linear regression function. A special procedure is introduced to generate a good starting point for solving global optimization problems at each iteration of the incremental algorithm. Such an approach allows one to find global or near global solution to the problem when the data sets are sufficiently dense. The algorithm is compared with the multistart Späth algorithm on several publicly available data sets for regression analysis. © 2013 Elsevier B.V. All rights reserved.
- Relation
- European Journal of Operational Research Vol. 229, no. 1 (2013), p. 132-142
- Rights
- Copyright 2013 Elsevier B.V.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Clusterwise linear regression; Incremental algorithm; Späth algorithm; Clusterwise linear regressions; Clusterwise regression; Global optimization problems; Global solutions; Nonconvex functions; Nonsmooth nonconvex optimization; Regression function; Algorithms; Global optimization; Iterative methods; Linear regression; Problem solving; MD Multidisciplinary
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