传统的特征提取方法主要有：主分量分析、奇异值分解、投影追踪、自组织映射等。
Classical feature extraction methods include: Principle Component Analysis, Singular Value Decomposition, Projection Pursuit, Self-Organizing Map, and so on.
提出了奇异值分解(SVD)和主分量分析(PCA)相结合的人脸识别算法。
A face recognition method based on the fusion of principal component analysis (PCA) and singular value decomposition (SVD) is presented.
其中人脸特征提取采用了奇异值分解和主分量分析法，身份验证则采用了以类内平均距离为判据的算法。
Here, we use the singular value decomposition and principal component analysis for facial feature extraction, using the average distance category as discrimination on the basis of authentication.
得到的电型并矢格林函数表达式由包括九个并矢分量的本征模展开式和一个奇异项组成。
It is shown that the expression of electric-type dyadic Green's function is the sum of an eigen-mode expansion containing nine dyadic components and a singular term.
得到的电型并矢格林函数表达式由包括九个并矢分量的本征模展开式和一个奇异项组成。
It is shown that the expression of electric-type dyadic Green's function is the sum of an eigen-mode expansion containing nine dyadic components and a singular term.
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