 Recurrent Spectral Network (RSN): Shaping a discrete map to reach automated classificationLorenzo Chicchi, Duccio Fanelli, Lorenzo Giambagli, Lorenzo Buffoni and Timoteo Carletti Chaos, Solitons & Fractals, 2023, vol. 168, issue C Abstract: A novel strategy to automated classification is introduced which exploits a fully trained dynamical system to steer items belonging to different categories towards distinct asymptotic target destinations. These latter are incorporated into the model by taking advantage of the spectral decomposition of the operator that rules the linear evolution across the processing network. Non-linear terms act for a transient and allow to disentangle the data supplied as initial condition to the discrete dynamical system. The system effectively aligns along assigned directions, which reflect the specificity of the provided input and that are encoded in the loss function via suitable spectral projections. The network can be equipped with several memory kernels which can be sequentially activated for serial datasets handling. Our novel approach to classification, that we here term Recurrent Spectral Network (RSN), is successfully challenged against a simple test-bed model, created for illustrative purposes, as well as a standard dataset for image processing training. Keywords: Machine learning; Dynamical systems; Spectral learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:168:y:2023:i:c:s0960077923000292 DOI: 10.1016/j.chaos.2023.113128 Access Statistics for this article Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros More articles in Chaos, Solitons & Fractals from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.