CAJ | 학술논문

在脑电图( EEG)信号识别中,EEG信号的采样环境、病人状态的多样性导致分类器训练所用的源域与分类器测试所用的目标域不匹配,分类器在目标域上表现不佳。为此,引入邻域适应策略,提出一种基于子空间相似度的改进主成分分析特征提取方法( SSM-PCA),在选择主成分时,考虑源域和目标域数据的几何和统计特性,并结合迁移学习分类器大间隔投射迁移支持向量机( LMPROJ),给出以SSM-PCA为基础的LMPROJ分类识别方法。实验结果表明,与结合PCA特征抽取技术和K近邻分类器实现的识别方法相比,该方法在识别正确率方面得到较大提升。
재뇌전도( EEG)신호식별중,EEG신호적채양배경、병인상태적다양성도치분류기훈련소용적원역여분류기측시소용적목표역불필배,분류기재목표역상표현불가。위차,인입린역괄응책략,제출일충기우자공간상사도적개진주성분분석특정제취방법( SSM-PCA),재선택주성분시,고필원역화목표역수거적궤하화통계특성,병결합천이학습분류기대간격투사천이지지향량궤( LMPROJ),급출이SSM-PCA위기출적LMPROJ분류식별방법。실험결과표명,여결합PCA특정추취기술화K근린분류기실현적식별방법상비,해방법재식별정학솔방면득도교대제승。
Many practical applications for epilepsy detection,the diversity of the health status of epilepsy patient and the timing of Electroencephalogram( EEG) signal measurements lead to the mismatching between the source domain used for classifier trained and target domain used for testing. The classifiers usually do not perform well on the target domain. In order to overcome this shortcoming,an improved Principal Component Analysis( PCA) feature extraction method called Subspace Similarity Measure Based Principal Component Analysis(SSM-PCA) is proposed,and a new classification method,named as SSM-PCA-LMPROJ is proposed by integrating SSM-PCA and the classical classifier Large Margin Projected Transductive Support Vector Machine ( LMPROJ ) . Experimental results show that the proposed method has obvious advantages compared with the traditional method,such as the method combining PCA and K Nearest Neighbor ( KNN) classifier.