Abstract
This work presents the results of the potential of band linear system solvers for improving the scalability of the Extreme Learning Machine (ELM) method at large model sizes. The model is tested on the standard MNIST dataset with a range of solvers provided by the SciPy Python library. The results are analyzed taking into consideration the overall performance and the performance impact of band solvers across different matrix bandwidths, as well as the performance versus runtime analysis. The findings show potential in applying the proposed method to very large ELM models with narrow band matrices.
Recommended Citation
A. Akusok et al., "Work-in-Progress: Evaluating Feasibility of Band Matrix Solvers for Scaling up Extreme Learning Machine Method," Lecture Notes in Networks and Systems, vol. 1662 LNNS, pp. 347 - 355, Springer Nature, Jan 2026.
The definitive version is available at https://doi.org/10.1007/978-3-032-07319-8_31
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Band solver; Computational complexity; Extreme learning machine; Linear system
International Standard Book Number (ISBN)
978-303207318-1
International Standard Serial Number (ISSN)
2367-3389; 2367-3370
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 Springer Nature, All rights reserved.
Publication Date
01 Jan 2026
