- Title
- Nonsmooth optimization algorithm for solving clusterwise linear regression problems
- Creator
- Bagirov, Adil; Ugon, Julien; Mirzayeva, Hijran
- Date
- 2015
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/92556
- Identifier
- vital:9601
- Identifier
-
https://doi.org/10.1007/s10957-014-0566-y
- Identifier
- ISSN:0022-3239
- Abstract
- Clusterwise linear regression consists of finding a number of linear regression functions each approximating a subset of the data. In this paper, the clusterwise linear regression problem is formulated as a nonsmooth nonconvex optimization problem and an algorithm based on an incremental approach and on the discrete gradient method of nonsmooth optimization is designed to solve it. This algorithm incrementally divides the whole dataset into groups which can be easily approximated by one linear regression function. A special procedure is introduced to generate good starting points for solving global optimization problems at each iteration of the incremental algorithm. The algorithm is compared with the multi-start Spath and the incremental algorithms on several publicly available datasets for regression analysis.
- Publisher
- Springer
- Relation
- Journal of Optimization Theory and Applications Vol. 164, no. 3 (2015), p. 755-780; http://purl.org/au-research/grants/arc/DP140103213
- Rights
- Copyright © Springer Science+Business Media New York 2014
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0102 Applied Mathematics; 0103 Numerical and Computational Mathematics; 0906 Electrical and Electronic Engineering; Nonsmooth optimization; Nonconvex optimization; Clusterwise linear regression; Discrete gradient method; GL Tham
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