An infrasound signal classification method based on non-negative matrix factorization

2021 ◽  
Vol 150 (4) ◽  
pp. A315-A315
Author(s):  
Zixuan Meng ◽  
Pengxiao Teng
Keyword(s):  
Matrix Factorization ◽  
Signal Classification ◽  
Classification Method ◽  
Infrasound Signal ◽  
Non Negative Matrix Factorization

scholarly journals Transferred Subspace Learning Based on Non-negative Matrix Factorization for EEG Signal Classification

2021 ◽  
Vol 15 ◽  
Author(s):  
Aimei Dong ◽  
Zhigang Li ◽  
Qiuyu Zheng
Keyword(s):  
Matrix Factorization ◽  
Feature Space ◽  
Signal Classification ◽  
Training Dataset ◽  
Eeg Signal ◽  
Practical Applications ◽  
Testing Dataset ◽  
Eeg Signal Classification ◽  
Original Feature ◽  
Non Negative Matrix Factorization

EEG signal classification has been a research hotspot recently. The combination of EEG signal classification with machine learning technology is very popular. Traditional machine leaning methods for EEG signal classification assume that the EEG signals are drawn from the same distribution. However, the assumption is not always satisfied with the practical applications. In practical applications, the training dataset and the testing dataset are from different but related domains. How to make best use of the training dataset knowledge to improve the testing dataset is critical for these circumstances. In this paper, a novel method combining the non-negative matrix factorization technology and the transfer learning (NMF-TL) is proposed for EEG signal classification. Specifically, the shared subspace is extracted from the testing dataset and training dataset using non-negative matrix factorization firstly and then the shared subspace and the original feature space are combined to obtain the final EEG signal classification results. On the one hand, the non-negative matrix factorization can assure to obtain essential information between the testing and the training dataset; on the other hand, the combination of shared subspace and the original feature space can fully use all the signals including the testing and the training dataset. Extensive experiments on Bonn EEG confirmed the effectiveness of the proposed method.

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