山东大学学报（工学版）
山東大學學報（工學版）
산동대학학보（공학판）
JOURNAL OF SHANDONG UNIVERSITY(ENGINEERING SCIENCE)
2014年
6期
70-76
,共7页
李发权%杨立才%颜红博
李髮權%楊立纔%顏紅博
리발권%양립재%안홍박
主成分分析%支持向量机%信息融合%情绪识别%特征子集
主成分分析%支持嚮量機%信息融閤%情緒識彆%特徵子集
주성분분석%지지향량궤%신식융합%정서식별%특정자집
principal component analysis%support vector machine%information fusion%emotion recognition%feature subset
为了有效解决情绪识别过程中多种生理信息融合所导致的运算量过大的问题，提出了一种主成分分析（principal component analysis，PCA）与支持向量机（support vector machine，SVM）相结合的情绪识别方法。利用主成分分析法，求出各特征对情绪识别效果的影响权重，通过阈值法选择权重较大的特征组成新的特征子集，从而减少SVM的输入特征维数，降低算法的运算量。试验结果表明，该方法可以有效提高算法的执行效率。
為瞭有效解決情緒識彆過程中多種生理信息融閤所導緻的運算量過大的問題，提齣瞭一種主成分分析（principal component analysis，PCA）與支持嚮量機（support vector machine，SVM）相結閤的情緒識彆方法。利用主成分分析法，求齣各特徵對情緒識彆效果的影響權重，通過閾值法選擇權重較大的特徵組成新的特徵子集，從而減少SVM的輸入特徵維數，降低算法的運算量。試驗結果錶明，該方法可以有效提高算法的執行效率。
위료유효해결정서식별과정중다충생리신식융합소도치적운산량과대적문제，제출료일충주성분분석（principal component analysis，PCA）여지지향량궤（support vector machine，SVM）상결합적정서식별방법。이용주성분분석법，구출각특정대정서식별효과적영향권중，통과역치법선택권중교대적특정조성신적특정자집，종이감소SVM적수입특정유수，강저산법적운산량。시험결과표명，해방법가이유효제고산법적집행효솔。
To reduce the complexity of the emotion-recognition algorithm caused by multiphysiological information fu-sion 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.
