Principle Component

主成分:在统计学和机器学习中

常用释义

词性释义

主成分:在统计学和机器学习中,主成分是指将多个相关变量转换为少数几个无关变量的过程。这些无关变量被称为主成分,它们能够解释原始数据中大部分的方差。
例句
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1·The analysis method includes linear regression and principle component analysis.
分析方法包括趋势分析、主分量分析。
2·Methods of principle component analysis, clustering analysis, comparative analysis and modeling were employed.
研究方法:主成分法,聚类分析法,比较法,模型法。
3·The extraction and application of principle component representations were studied for machine noise monitoring.
研究了设备噪声监测中主分量特征表示的提取及应用。
4·On the basis of this, this paper suggests that analytic hierarchy process(AHP) and principle component analysis(PCA) can .
本文提出一种基于主成分分析法的动态神经网络模型实现高炉铁水含硅量多步预报。
5·Finally, based on the principle component of the descriptors, a cascade filtering is designed to speed up the feature matching.
最后根据描述子主成分的差异设计层叠分类器,加速特征匹配。
6·The principle component 1 in Carex lasiocarpa could explain the species relative density of 88% and the aboveground biomass of 84%.
毛果苔草湿地中主成分1能够解释88%的物种相对密度和84%的地上生物量;
7·Results:The principle component of standardized correlation matrix does not represent the variance weight of gene frequency matrix.
标准化相关阵的主成分反映的仅是“相关信息量权”,不包括“方差信息量权”。
8·Principle component analysis showed hat the elevation and soil thickness can be used as sensitive indices to describe plant change.
主成分分析结果表明,海拔和土壤厚度是指示植被变化的敏感指标;
9·The features concerned are such as texture feature, gray histogram feature and features derive form the principle component analysis.
在图像特征提取上改进并提出了三种特征的提取:纹理特征,灰度直方图均值化特征,图像的主成分特征。
10·In this paper, the principle component analysis (PCA) theory is introduced, and the theory is used for fault diagnosis of lock of actuator.
文中介绍了主元分析算法以及在故障检测方面的应用。