Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In thi...Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method.展开更多
为提高水电机组故障诊断的准确性、确保机组安全运行,提出了基于局部切空间排列算法(local tangent space alignment,LTSA)与谱聚类的水电机组振动故障诊断方法。首先,利用GHM(Geronimo,Hardin,Massopust)多小波对机组振动信号进行分解...为提高水电机组故障诊断的准确性、确保机组安全运行,提出了基于局部切空间排列算法(local tangent space alignment,LTSA)与谱聚类的水电机组振动故障诊断方法。首先,利用GHM(Geronimo,Hardin,Massopust)多小波对机组振动信号进行分解,从所得多小波分解系数中提取多个特征参数,并利用检测指数(detection index,DI)对特征参数进行特征选择,降低特征维数;然后,利用LTSA对所得故障特征进行融合,获取低维强敏感特征参数;最后,将所得的特征参数输入到谱聚类算法中,实现水电机组振动故障识别。利用转子试验台和水电机组振动信号对所提出的方法进行了验证,结果表明该方法能够更好地识别机组故障。展开更多
基金the National Natural Science Foundation of China(No.61004088)the Key Basic Research Foundation of Shanghai Municipal Science and Technology Commission(No.09JC1408000)
文摘Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method.
文摘为提高水电机组故障诊断的准确性、确保机组安全运行,提出了基于局部切空间排列算法(local tangent space alignment,LTSA)与谱聚类的水电机组振动故障诊断方法。首先,利用GHM(Geronimo,Hardin,Massopust)多小波对机组振动信号进行分解,从所得多小波分解系数中提取多个特征参数,并利用检测指数(detection index,DI)对特征参数进行特征选择,降低特征维数;然后,利用LTSA对所得故障特征进行融合,获取低维强敏感特征参数;最后,将所得的特征参数输入到谱聚类算法中,实现水电机组振动故障识别。利用转子试验台和水电机组振动信号对所提出的方法进行了验证,结果表明该方法能够更好地识别机组故障。
