JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2014, Vol. 44 ›› Issue (6): 70-76.doi: 10.6040/j.issn.1672-3961.0.2014.120

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An emotion recognition method of multiphysiological information fusion based on PCA-SVM

LI Faquan, YANG Licai, YAN Hongbo   

  1. School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China
  • Received:2014-04-23 Revised:2014-09-24 Online:2014-12-20 Published:2014-04-23

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Abstract: To reduce the complexity of the emotion-recognition algorithm caused by multiphysiological information fusion an emotion recognition method based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) was proposed. The influential weights of emotion recognition were calculated for initial features by the PCA, and the features of which the weights were larger than a certain threshold were selected to compose the new feature set. Thus the dimension of the classifierinputs could be reduced so that the complexity of the algorithm will be simplified. Experimental results showed that the PCA-SVM algorithm for sentiment analysis could effectively improve the efficiency of emotion recognition.

Key words: information fusion, principal component analysis, support vector machine, emotion recognition, feature subset

CLC Number: 

  • TP391.3
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