A1 Journal article (refereed)
A Douglas–Rachford method for sparse extreme learning machine (2019)


Kärkkäinen, T., & Glowinski, R. (2019). A Douglas–Rachford method for sparse extreme learning machine. Methods and Applications of Analysis, 26(3), 217-234. https://doi.org/10.4310/MAA.2019.v26.n3.a1


JYU authors or editors


Publication details

All authors or editorsKärkkäinen, Tommi; Glowinski, Roland

Journal or seriesMethods and Applications of Analysis

ISSN1073-2772

eISSN1945-0001

Publication year2019

Volume26

Issue number3

Pages range217-234

PublisherInternational Press

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.4310/MAA.2019.v26.n3.a1

Publication open accessNot open

Publication channel open access


Abstract

Operator-splitting methods have gained popularity in various areas of computational sciences, including machine learning. In this article, we present a novel nonsmooth and nonconvex formulation and its efficient associated solution algorithm to derive a sparse predictive machine learning model. The model structure is based on the so-called extreme learning machine with randomly generated basis. Our computational experiments confirm the efficiency of the proposed method, when a bold selection of the timestep is made. Comparative tests also indicate interesting results concerning the use of the l0 seminorm for ultimate sparsity.


Keywordsmachine learningmathematical optimisation

Free keywordsoperator-splitting; Douglas–Rachford; extreme learning machine; sparse regularization


Contributing organizations


Ministry reportingYes

Reporting Year2020

JUFO rating1


Last updated on 2024-27-02 at 13:06