Work-in-Progress: Evaluating Feasibility of Band Matrix Solvers for Scaling up Extreme Learning Machine Method

: Akusok, Anton; Björk, Kaj-Mikael; Lendasse, Amaury; Espinosa Leal, Leonardo

: Auer, Michael E.; Langmann, Reinhard; May, Dominik; Morales, Manuel

: International Conference on Smart Technologies & Education

Publisher: Springer Nature Switzerland

: 2026

Lecture Notes in Networks and Systems

: Smart Technologies for an All-Electric Society : Proceedings of the 22nd International Conference on Smart Technologies & Education (STE2025). Volume 2

: 1662

: 347

: 355

: 978-3-032-07318-1

: 978-3-032-07319-8

: 2367-3370

: 2367-3389

DOI: https://doi.org/10.1007/978-3-032-07319-8_31

: https://doi.org/10.1007/978-3-032-07319-8_31

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.