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dc.contributor.authorLeon-Medina, Jersson Xavier
dc.contributor.authorAnaya Vejar, Maribel
dc.contributor.authorPozo Montero, Francesc
dc.contributor.authorTibaduiza Burgos, Diego Alexander
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.date.accessioned2020-09-04T12:55:52Z
dc.date.available2020-09-04T12:55:52Z
dc.date.issued2020-08-27
dc.identifier.citationLeon-Medina, J.X. [et al.]. Nonlinear feature extraction through manifold learning in an electronic tongue classification task. "Sensors", 27 Agost 2020, vol. 2020, núm. 20, p. 1-20.
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/2117/328418
dc.description.abstractA nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.
dc.format.extent20 p.
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística
dc.subject.lcshMachine learning
dc.subject.lcshArtificial intelligence
dc.subject.otherManifold learning
dc.subject.otherFeature extraction
dc.subject.otherClassification
dc.subject.otherElectronic tongue
dc.subject.otherMachine learning
dc.subject.othert-SNE
dc.subject.otherLTSA
dc.subject.otherIsomap
dc.subject.otherLocally linear embedding
dc.titleNonlinear feature extraction through manifold learning in an electronic tongue classification task
dc.typeArticle
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacIntel·ligència artificial
dc.contributor.groupUniversitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
dc.identifier.doi10.3390/s20174834
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/20/17/4834/htm
dc.rights.accessOpen Access
local.identifier.drac29187410
dc.description.versionPostprint (published version)
local.citation.authorLeon-Medina, J.X.; Anaya, M.; Pozo, F.; Tibaduiza, Diego Alexander
local.citation.publicationNameSensors
local.citation.volume2020
local.citation.number20
local.citation.startingPage1
local.citation.endingPage20


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain