Name
Title | ||
|---|---|---|
Data-based analysis of Laplacian Eigenmaps for manifold reduction in supervised Liquid State classifiers. |
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 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ins.2018.11.017 | Information Sciences |
Keywords | Field | DocType |
Reservoir computing,Classification,Laplacian,Manifold reduction | External Data Representation,Hyperparameter,Pattern recognition,Artificial intelligence,Manifold,Machine learning,Mathematics,Laplace operator,Statistical analysis | Journal |
Volume | ISSN | Citations |
478 | 0020-0255 | 0 |
PageRank | References | Authors |
0.34 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paolo Arena | 1 | 0 | 1.35 |
| Luca Patané | 2 | 104 | 17.31 |
| Angelo Giuseppe Spinosa | 3 | 0 | 0.68 |