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.
针对减速箱运行状态和特征参数之间存在的复杂非线性关系，提出了基于主成分分析的RBF神经网络减速箱运行状态诊断方法。
As to the complicated nonlinear relation existing between running status of gear reducer and characteristic parameters, PCA-based RBF neural network reducer running status diagnostics is put forward.
论文中重点介绍了该种方法的降维思想，以及用主成分分析方法、对应分析方法和非线性映射方法解决问题的步骤。
In this paper, the emphasis is placed on the technique for reducing the dimensions. The principal analysis, correspondence analysis and nonlinear mapping are described in detail.
主成分分析方法主要利用数据的线性相关性来降维，并不适合非线性相关的情况。
As principal component analysis mainly use the linear correlation of the data, we propose a nonlinear principal component analysis method, by combining the mercer kernel function with it.
在此基础上，本文引入了分析有序数据的非线性主成分方法。
On this basis, this paper introduce an analysis of ordinal data on nonlinear principal component method.
在此基础上，本文引入了分析有序数据的非线性主成分方法。
On this basis, this paper introduce an analysis of ordinal data on nonlinear principal component method.
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