[自][数] 主成分分析
Principal component analysis was carried out to study the slaughtering performance of cross combinations using Anas platyrhynchos as male parent.
利用主成分分析对以绿头野鸭为父本的不同杂交组合的屠宰性能进行了研究。
The comparison items of geographical elements covered floristic spectrum and its Principal Component Analysis (PCA) method as well as its hierarchical cluster analysis.
其中，区系地理成分的对比又包括：植物区系谱系，主成分分析（PCA）和聚类图分析；
Principal component analysis (PCA) and correlative component analysis (CCA) are commonly used techniques to extract components.
主成分分析和相关成分分析都是成分提取的有效方法。
Principal component analysis is a linear method, but the most data are nonlinear.
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 (PCA) can not be used to detect the *****eration of camellia seed oil, but can be used in detection of *****eration in sesame oil.
主成分分析（PCA）对山茶油与大豆油及其混合物检测效果较差，对芝麻油、大豆油及两者混合物取得了较好的检测效果；
Cluster analysis and principal component analysis on data of anthropometry from Asian populations showed that Asian populations could be divided into north populations and south populations.
本文通过对亚洲群体体部测量资料的聚类分析与主成分分析，认为亚洲群体可分为北方类群与南方类群。
Principal component analysis and stepwise regression were employed to characterize availability of heavy metals, microbial responses and their effect factors in the stu***d soil.
利用主成分和逐步回归分析了影响土壤重金属的有效性及其微生物学效应的因素。
Principal component analysis indicated that panicle length factor had very significantly positive linear relationship to yield.
主成分分析表明：穗长因子对产量有极显著正线性关系；
Methods The methods of analysis were used by expert consulting graded approach , discrete tendency , principal component analysis and cluster analysis.
方法采用专家咨询打分法、离散趋势法、主成分分析法和聚类分析法进行分析。
One way to improve the robustness of principal component analysis is stu***d in order to increase the accuracy of PCA algorithm.
夏绍玮，王松主要研究了改善主成分分析算法鲁棒性的一种实现途径，以提高PCA的精度。
Drought resistance of 202 soybean materials was evaluated by using the method of principal component analysis according to relative drought resistance coefficient of 10 indexes at harvest stage.
以收获期10项指标的相对抗旱系数为基础，应用主成分分析方法对黑龙江省202份大豆基因型进行抗旱性评价研究。
Through principal component analysis, we have synthesized numerous indexes, (eliminated) information overlapping of the sample, and reduced the input dimension of BP network.
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。
It can obtain better result by the way of constellation graphics than by the way of combining principal component analysis with fuzzy clustering method.
采用星座图法对森林景观生态功能进行分区比利用主成分分析与模糊聚类相结合方法能得到更为满意的结果。
Then, this paper synthetically evaluates the comprehensive strength of patent about more developed 10 areas of our country by using the principal component analysis.
在此基础上，运用主成分分析方法对我国10个经济较发达地区的专利实力进行了分析评价。
Then the basic concepts of principal component analysis and partial least square regression are stu***d, as well as their advantages and disadvantages.
引入了主成分分析与偏最小二乘回归等多元统计与回归方法，并分析了其基本思想与优缺点。
Firstly, the spectrum data is processed by principal component analysis(PCA), then, an estimating model based on a Gaussian kernel function is set up using the PCA data and their temperatures.
首先对历史光谱数据进行主成分分析（PCA）处理，再根据PCA特征数据与其表面温度的对应关系建立温度的估计模型，该模型是基于高斯核函数的。
The principal component analysis can determine a few principal components from the connected factors and evaluate the all-around qualities of students.
采用主成分分析方法，从多种影响因素中找出几个主要成分指标，较为全面地综合评价了学生素质。
The principal component analysis(PCA) approach for sensor fault detection, identification and reconstruction in HVAC system is presented.
提出了空调系统传感器故障检测、故障识别、故障重构的主成分分析方法。
Methods We used principal component analysis and factor analysis to evaluate the 6 hospital's management quality.
方法用主成分分析与因子分析法对6所医院的管理工作质量进行综合评价。
The principal component analysis in multivariate statistic analysis is a method of compressing the dimension of vector data by extracting principal typical components from sample data set.
多元统计分析的主成份分析方法是对多维矢量数据提取主要特征分量，以此达到压缩矢量维数的目的。
Conclusion Practice presents that principal component analysis and factor analysis both are suitable for synthetic evaluation of hospital management quality.
结论理论和实践表明主成分分析与因子分析法适宜于客观地综合评价医院管理工作质量。
Competitive power evaluation index system of towns in Shijiazhuang city is constructed by using principal component analysis method.
针对石家庄市域城镇发展现状，利用主成分分析方法，构建了竞争力评价指标体系。
To improve the running efficiency of the algorithm, the stochastic mapping of the patterns was modified based on principal component analysis.
为了提高群体智能聚类算法的运行效率，提出了利用主成分分析改善模式投影时的随机性。
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.
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。
主成分分析（Principal Component Analysis，PCA）是一种基于统计学的无监督降维方法，其核心目标是将高维数据转换为低维表示，同时保留数据的主要变化特征。该方法通过正交变换将可能存在相关性的原始变量转换为线性无关的主成分，并按方差贡献度从高到低排序。
数学原理 PCA的数学基础是协方差矩阵的特征值分解。假设数据集$X$已标准化处理，协方差矩阵为： $$ C = frac{1}{n-1}X^T X $$ 求解该矩阵的特征方程$Cv = lambda v$，其中$lambda$为特征值，对应的特征向量$v$即为主成分方向。第$k$个主成分的方差贡献率为$lambdak/sum{i=1}^p lambda_i$，通常选取累计贡献率超过85%的前$m$个主成分。
核心特征
典型应用场景
优势与局限 该方法计算复杂度为$O(p)$（$p$为特征维度），当样本量远大于特征数时效率较高，但需注意数据标准化预处理的重要性。对于非线性数据结构，建议结合核方法（Kernel PCA）使用。
来源参考：
主成分分析（Principal Component Analysis，PCA）是一种无监督的统计方法，主要用于数据降维和特征提取。其核心目标是通过线性变换将高维数据投影到低维空间，同时保留数据中的主要信息。
设数据矩阵(X)标准化后，协方差矩阵为： $$ C = frac{1}{n-1} X^T X $$ 通过特征分解(C = V Lambda V^T)，其中(Lambda)为特征值对角矩阵，(V)为特征向量矩阵。降维后的数据为： $$ Y = X V_k $$ 其中(V_k)为前(k)个特征向量组成的矩阵。
例如，若某数据集有100个特征，通过PCA发现前5个主成分解释了95%的方差，则可仅用这5个维度进行后续分析，大幅简化计算。
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