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dc.date.accessioned 2021-06-02T16:40:48Z
dc.date.available 2021-06-02T16:40:48Z
dc.date.issued 2020
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/119641
dc.description.abstract In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive useful supervised or unsupervised classification methods. Correct handling of incomplete, noisy or small datasets in machine learning is a fundamental and classic challenge. In this article, we provide a unified review of recently proposed methods based on signal decomposition for missing features imputation (data completion), classification of noisy samples and artificial generation of new data samples (data augmentation). We illustrate the application of these signal decomposition methods in diverse selected practical machine learning examples including: brain computer interface, epileptic intracranial electroencephalogram signals classification, face recognition/verification and water networks data analysis. We show that a signal decomposition approach can provide valuable tools to improve machine learning performance with low quality datasets. en
dc.language en es
dc.subject Empirical mode decomposition es
dc.subject Machine learning es
dc.subject Sparse representations es
dc.subject Tensor decomposition es
dc.subject Tensor completion es
dc.title Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets en
dc.type Articulo es
sedici.identifier.other https://doi.org/10.3390/app10238481 es
sedici.identifier.issn 2076-3417 es
sedici.creator.person Caiafa, Cesar Federico es
sedici.subject.materias Ingeniería Electrónica es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Instituto Argentino de Radioastronomía es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution 4.0 International (CC BY 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by/4.0/
sedici.description.peerReview peer-review es
sedici.relation.journalTitle Applied Sciences es
sedici.relation.journalVolumeAndIssue vol. 10, no. 23 es

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Creative Commons Attribution 4.0 International (CC BY 4.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International (CC BY 4.0)