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

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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Bibliographic Details
Main Authors: Cesar Federico Caiafa, Jordi Solé-Casals, Pere Marti-Puig, Sun Zhe, Toshihisa Tanaka
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Applied Sciences
Subjects:
empirical mode decomposition
machine learning
sparse representations
tensor decomposition
tensor completion
Online Access:https://www.mdpi.com/2076-3417/10/23/8481

Internet

https://www.mdpi.com/2076-3417/10/23/8481

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