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

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 analy...

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Bibliographic Details
Published in:Information sciences 2019-04, Vol.478, p.28-39
Main Authors: Arena, Paolo, Patanè, Luca, Spinosa, Angelo Giuseppe
Format: Article
Language:English
Subjects:
Classification
Laplacian
Manifold reduction
Reservoir computing
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Summary: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.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2018.11.017