Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets

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...

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Detalles Bibliográficos
Autor principal: Caiafa, Cesar Federico
Formato: Articulo
Lenguaje:Inglés
Publicado: 2020
Materias:
Ingeniería Electrónica
Ciencias Informáticas
Empirical mode decomposition
Machine learning
Sparse representations
Tensor decomposition
Tensor completion
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/119641
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-119641
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ingeniería Electrónica
Ciencias Informáticas
Empirical mode decomposition
Machine learning
Sparse representations
Tensor decomposition
Tensor completion
spellingShingle Ingeniería Electrónica
Ciencias Informáticas
Empirical mode decomposition
Machine learning
Sparse representations
Tensor decomposition
Tensor completion
Caiafa, Cesar Federico
Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
topic_facet Ingeniería Electrónica
Ciencias Informáticas
Empirical mode decomposition
Machine learning
Sparse representations
Tensor decomposition
Tensor completion
description 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.
format Articulo
Articulo
author Caiafa, Cesar Federico
author_facet Caiafa, Cesar Federico
author_sort Caiafa, Cesar Federico
title Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
title_short Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
title_full Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
title_fullStr Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
title_full_unstemmed Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
title_sort decomposition methods for machine learning with small, incomplete or noisy datasets
publishDate 2020
url http://sedici.unlp.edu.ar/handle/10915/119641
work_keys_str_mv AT caiafacesarfederico decompositionmethodsformachinelearningwithsmallincompleteornoisydatasets
bdutipo_str Repositorios
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