对多维动态数据系统给出了全局主成分分析(GPCA)模型，并对时序立体数据表进行立体式的综合与简化。
Global principal components analysis (GPCA) model is presented to the multidimensional dynamic data system and the time sequence solid data table is three-dimensionally synthesized and simplified.
主成分变换可以很好的压缩信息，但由于它的全局性，使得光谱特征和局部特征没有被更好的保留下来；
PCA is sufficient for reducing data volume, however it fails to preserve the spectral and local characteristic of the original data.
针对二维主成分分析（2DPCA）提取的是人脸的全局特征，但局部特征对人脸识别的作用非常大，提出了一种基于局部特征的自适应加权2DPCA。
From researching on the universal principle of feature fusion of image, a new algorithm was proposed which based on the 2 dimension principal component analyses(for short 2DPCA).
针对二维主成分分析（2DPCA）提取的是人脸的全局特征，但局部特征对人脸识别的作用非常大，提出了一种基于局部特征的自适应加权2DPCA。
From researching on the universal principle of feature fusion of image, a new algorithm was proposed which based on the 2 dimension principal component analyses(for short 2DPCA).
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