The manuscript introduces a data-driven technique founded on Laplacian Eigenmaps for manifold reduction in bio-inspired Liquid State classifiers. Starting from a preliminary in- troduction about the algorithm and the need of using manifold reduction methods for data representation, a statistical analysis of hyperparameters involved in the Laplacian Eigen- maps technique is presented and the effects of quantisation on trained weights is dis- cussed with a view to efficiently implement multiple parallel mappings in the digital do- main.

Data-based analysis of Laplacian Eigenmaps for manifold reduction in supervised Liquid State classifiers

Patane L.
;
2019-01-01

Abstract

The manuscript introduces a data-driven technique founded on Laplacian Eigenmaps for manifold reduction in bio-inspired Liquid State classifiers. Starting from a preliminary in- troduction about the algorithm and the need of using manifold reduction methods for data representation, a statistical analysis of hyperparameters involved in the Laplacian Eigen- maps technique is presented and the effects of quantisation on trained weights is dis- cussed with a view to efficiently implement multiple parallel mappings in the digital do- main.
2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3147917
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