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 |
Autori interni: | |
Data di pubblicazione: | 2019 |
Rivista: | |
Handle: | http://hdl.handle.net/20.500.11769/361487 |
Appare nelle tipologie: | 1.1 Articolo in rivista |
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