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.

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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.

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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.

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