Limited Memory Bundle Method for Clusterwise Linear Regression

Title: Limited Memory Bundle Method for Clusterwise Linear Regression
Creator: Karmitsa, Napsu; Bagirov, Adil; Taheri, Sona; Joki, Kaisa
Date: 2022
Type: Text; Book chapter
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/177904
Identifier: vital:15355
Identifier: https://doi.org/10.1007/978-3-030-70787-3_8
Identifier: ISBN:2213-8986 (ISSN)
Abstract: A clusterwise linear regression problem consists of finding a number of linear functions each approximating a subset of the given data. In this paper, the limited memory bundle method is modified and combined with the incremental approach to solve this problem using its nonsmooth optimization formulation. The main contribution of the proposed method is to obtain a fast solution time for large-scale clusterwise linear regression problems. The proposed algorithm is tested on small and large real-world data sets and compared with other algorithms for clusterwise linear regression. Numerical results demonstrate that the proposed algorithm is especially efficient in data sets with large numbers of data points and input variables. © 2022, Springer Nature Switzerland AG.
Publisher: Springer Science and Business Media B.V.
Relation: Intelligent Systems, Control and Automation: Science and Engineering p. 109-122
Rights: All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
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