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.
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.
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.
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。
Experimental results show that principal curve component analysis is excellent for solving nonlinear principal component problem, and it has great applications potentials.
运用非线性主成分分析法对欧亚地区1948—2007年冬季海平面气压距平场进行分析。
Eurasian winter sea level pressure anomalies during 1948-2007 were investigated by applying a nonlinear principal component analysis (NLPCA) method.
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.
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.
依据鄱阳湖地区1949 ~ 2002年耕地面积和社会经济统计数据，运用主成分分析和多元线性回归模型等统计方法分析该地区耕地面积变化的驱动因素。
Based on statistical data of cultivated land and social and economic factors from 1949 to 2002 in Poyang Lake region, this paper discusses the driving forces by multi-variable statistical method.
著名的线性变换的方法包括，例如，主成分分析，因子分析，投影寻踪。
Well-known linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit.
运用非线性主成分分析法对欧亚地区1948—2007年冬季海平面气压距平场进行分析。
Eurasian summer sea level pressure anomalies during 1948 -2007 were investigated by applying a Nonlinear Principal Component Analysis (NLPCA) method.
每一个主成分都是原始变量的线性组合，主成分之间互为正交关系，在剔除冗余信息的同时，通过主成分分析的降维，解决光谱数据的存储和处理问题。
Every principle component is the linear combination of the original variables and is irrelevant to each other. The spectra data can be stored and dealt with computer by reducing…
每一个主成分都是原始变量的线性组合，主成分之间互为正交关系，在剔除冗余信息的同时，通过主成分分析的降维，解决光谱数据的存储和处理问题。
Every principle component is the linear combination of the original variables and is irrelevant to each other. The spectra data can be stored and dealt with computer by reducing…
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