通过主成分分析法将众多指标进行综合，消除样本间的信息重叠，降低BP网络的输入维数。
Through principal component analysis, we have synthesized numerous indexes, (eliminated) information overlapping of the sample, and reduced the input dimension of BP network.
并对影响城市空气质量的5个主要因素进行主成分分析，找出最能代表原来数据信息的2至3个因子代替原来的5个变量。
Then 5 main factors affecting urban air quality to principal component analysis identify the most representative of the original data instead of the 2 to 3 factors 5 variables.
本文提出一种利用平行坐标图的多元信息表示对主成分分析特征提取方法进行优化的分类技术。
A novel method for optimizing the principle component analysis in feature extraction is proposed, which making use of parallel coordinate plot for graphical presentation of multivariate information.
针对上述问题，提出了信息增益(IG)与主成分分析(PCA)相结合的特征选择方法。
Aiming at the preceding problem, this paper puts forward a feature selection method using Information Gain (IG) and Principle Component (Analysis) (PCA).
运用主成分分析方法，经过矩阵变换，提取振动信号的主要信息，从而实现对振动噪声源的分析。
The main vibration signals can be obtained by reducing its dimensions through matrix conversion in the principal component analyzed, and realize the analysis to the vibration noise source.
主成分分析法是从观测数据中获取主要信息的一种多变量统计方法。
Principal Component Analysis (PCA) is a main multivariate statistical method for getting principal information from observational data.
通过对样本数据空间的主成分分析，能够保证在信息损失最少的情况下，对高维变量空间进行降维处理，减少样本数据间的相关性。
To analysis the sample data space by PCA can assume that it can lower the dimension of high variant space and eliminate the relativity of sample data.
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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