The manuscript introduces a data-driven technique founded on Laplacian Eigenmaps for manifold reduction in bio-inspired Liquid State classifiers. Starting from a preliminary introduction about the algorithm and the need of using manifold reduction methods for data representation, a statistical analysis of hyperparameters involved in the Laplacian Eigenmaps technique is presented and the effects of quantisation on trained weights is discussed with a view to efficiently implement multiple parallel mappings in the digital domain.
|Titolo:||Data-based analysis of Laplacian Eigenmaps for manifold reduction in supervised Liquid State classifiers|
PATANE', LUCA (Corresponding)
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||1.1 Articolo in rivista|