传统的基于线性变换的主成分分析法(PCA)是一种有效的地震属性降维优化方法。

Traditional principal analysis method (PCA) based on linear transform is effective method of seismic attribute dimension-reducing optimization.

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基于核主成分分析（KPCA）的人脸识别算法能够提取非线性图像特征，在小样本训练条件下有较好性能。

The algorithm of face recognition based on kernel principal component analysis(KPCA)can abstract nonlinear features of image and can get better performance under less sample training conditions.

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线性主成分分析是一种线性分析方法，而数据通常是非线性的。

Principal component analysis is a linear method, but the most data are nonlinear.

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