Affective computing draws more and more attention to the human-computer interaction. Based on physiological signals acquired by body sensor network, within the affection recognition process, the problem that training samples have larger class distance and smaller intraclass distance must be considered. For the class divisibility and intraclass compactness problem, researching method of samples validity was proposed based on metric multidimensional scaling. With dissimilarity matrix, scalar product matrix was calculated. Subsequently, individual attribute reconstructing matrix could be got using principal components factor analysis to display samples difference in low dimension. By means of experiment results, training and testing samples for sentiment classifier will be selected instructionally.
['Wei Wang', 'Xiaodan Huang']
Divisibility and Compactness Analysis of Physiological Signals for Sentiment Classification in Body Sensor Network