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

• Autores: Caiafa, César Federico; Solé Casals, Jordi; Marti Puig, Pere; Zhe, Sun; Tanaka, Toshihisa
• Año de publicación: 2021
• Idioma: inglés
• Tipo de recurso: parte de libro
• Materia: machine learning; incomplete data
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

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.
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Solé Casals, Jordi. University of Vic; España
Fil: Marti Puig, Pere. University of Vic; España
Fil: Zhe, Sun. Head Office for Information Systems and Cybersecurity. Computational Engineering Applications Unit. RIKEN; Japón
Fil: Tanaka, Toshihisa. Tokyo University of Agriculture and Technology; Japón