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 editors: Kärkkäinen, Tommi; Glowinski, Roland
Journal or series: Methods and Applications of Analysis
ISSN: 1073-2772
eISSN: 1945-0001
Publication year: 2019
Volume: 26
Issue number: 3
Pages range: 217-234
Publisher: International Press
Publication country: United States
Publication language: English
DOI: https://doi.org/10.4310/MAA.2019.v26.n3.a1
Publication open access: Not 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.
Keywords: machine learning; mathematical optimisation
Free keywords: operator-splitting; Douglas–Rachford; extreme learning machine; sparse regularization
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 1