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Nonsmooth nonconvex optimization approach to clusterwise linear regression problems

journal contribution
posted on 2013-08-16, 00:00 authored by A M Bagirov, Julien UgonJulien Ugon, H Mirzayeva
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.

History

Journal

European journal of operational research

Volume

229

Issue

1

Pagination

132 - 142

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

0377-2217

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2013, Elsevier B.V.

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