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Review

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

1
Instituto Argentino de Radioastronomía—CCT La Plata, CONICET/CIC-PBA/UNLP, 1894 V. Elisa, Argentina
2
Tensor Learning Team—Center for Advanced Intelligence Project, RIKEN, Tokyo 103-0027, Japan
3
Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, 08500 Vic, Catalonia, Spain
4
Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Wako-Shi 351-0198, Japan
5
Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(23), 8481; https://doi.org/10.3390/app10238481
Received: 31 October 2020 / Revised: 23 November 2020 / Accepted: 24 November 2020 / Published: 27 November 2020
(This article belongs to the Special Issue Machine Learning Methods with Noisy, Incomplete or Small 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 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. View Full-Text
Keywords: empirical mode decomposition; machine learning; sparse representations; tensor decomposition; tensor completion empirical mode decomposition; machine learning; sparse representations; tensor decomposition; tensor completion
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MDPI and ACS Style

Caiafa, C.F.; Solé-Casals, J.; Marti-Puig, P.; Zhe, S.; Tanaka, T. Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets. Appl. Sci. 2020, 10, 8481. https://doi.org/10.3390/app10238481

AMA Style

Caiafa CF, Solé-Casals J, Marti-Puig P, Zhe S, Tanaka T. Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets. Applied Sciences. 2020; 10(23):8481. https://doi.org/10.3390/app10238481

Chicago/Turabian Style

Caiafa, Cesar F., Jordi Solé-Casals, Pere Marti-Puig, Sun Zhe, and Toshihisa Tanaka. 2020. "Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets" Applied Sciences 10, no. 23: 8481. https://doi.org/10.3390/app10238481

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