Experimental results show that principal curve component analysis is excellent for solving nonlinear principal component problem, and it has great applications potentials.
仿真实验结果表明，主曲线成分分析能很好地解决非线性主成分问题，应用前景广阔。
This second edition has been completely revised to feature new chapters on principal component analysis, self-modeling curve resolution, and multi-way analysis methods.
这次再版已经被完全修正成特征关于主成分分析，自我模型化曲线决定和多模式的分析方法的新章。
This paper considers the classification compression principal component estimate of regression coefficient in growth curve model and proves that it is superior to least squares estimate.
研究岭型主成分估计在降维估计类中的方差最优性，证明了它的方差阵在降维估计类中最小，方差阵的特征值最小，方差和及方差积最小。
This paper considers the classification compression principal component estimate of regression coefficient in growth curve model and proves that it is superior to least squares estimate.
研究岭型主成分估计在降维估计类中的方差最优性，证明了它的方差阵在降维估计类中最小，方差阵的特征值最小，方差和及方差积最小。
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