EEG based emotion recognition using fusion feature extraction method

2020 ◽  
Vol 79 (37-38) ◽  
pp. 27057-27074 ◽  
Author(s):  
Qiang Gao ◽  
Chu-han Wang ◽  
Zhe Wang ◽  
Xiao-lin Song ◽  
En-zeng Dong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
Fusion Feature

A new proposed statistical feature extraction method in speech emotion recognition

2021 ◽  
Vol 93 ◽  
pp. 107172
Author(s):  
Husam Ali Abdulmohsin ◽  
Hala Bahjat Abdul wahab ◽  
Abdul Mohssen Jaber Abdul hossen
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Extraction Method ◽  
Speech Emotion Recognition ◽  
Feature Extraction Method ◽  
Statistical Feature

scholarly journals A New GNSS-R Altimetry Algorithm Based on Machine Learning Fusion Model and Feature Optimization to Improve the Precision of Sea Surface Height Retrieval

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiang Wang ◽  
Wei Zheng ◽  
Fan Wu ◽  
Aigong Xu ◽  
Huizhong Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Extraction Method ◽  
Weighted Average ◽  
Leading Edge ◽  
Sea Surface ◽  
Fusion Model ◽  
Feature Extraction Method ◽  
Fusion Feature ◽  
New Machine

The global navigation satellite system reflectometer (GNSS-R) can improve the observation and inversion of mesoscale by increasing the spatial coverage of ocean surface observations. The traditional retracking method is an empirical model with lower accuracy and condenses the Delay-Doppler Map information to a single scalar metric cannot completely represent the sea surface height (SSH) information. Firstly, to use multi-dimensional inputs for SSH retrieval, this paper constructs a new machine learning weighted average fusion feature extraction method based on the machine learning fusion model and feature extraction, which takes airborne delay waveform (DW) data as input and SSH as output. R2-Ranking method is used for weighted fusion, and the weights are distributed by the coefficient of determination of cross validation on the training set. Moreover, based on the airborne delay waveform data set, three features that are sensitive to the height of the sea surface are constructed, including the delay of the 70% peak correlation power (PCP70), the waveform leading edge peak first derivative (PFD), and the leading edge slope (LES). The effect of feature sets with varying levels of information details are analyzed as well. Secondly, the global average sea surface DTU15, which has been corrected by tides, is used to verify the reliability of the new machine learning weighted average fusion feature extraction method. The results show that the best retrieval performance can be obtained by using DW, PCP70 and PFD features. Compared with the DTU15 model, the root mean square error is about 0.23 m, and the correlation coefficient is about 0.75. Thirdly, the retrieval performance of the new machine learning weighted average fusion feature extraction method and the traditional single-point re-tracking method are compared and analyzed. The results show that the new machine learning weighted average fusion feature extraction method can effectively improve the precision of SSH retrieval, in which the mean absolute error is reduced by 63.1 and 59.2% respectively, and the root mean square error is reduced by 63.3 and 61.8% respectively; The correlation coefficient increased by 31.6 and 44.2% respectively. This method will provide the theoretical method support for the future GNSS-R SSH altimetry verification satellite.

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