By principal component analysis, we get four main components.
通过主成分分析,得到了4个主成分。
参考来源 - 基于主成分分析法的中国外贸增长能力综合评价—《价值工程》—2010年第31期—龙源期刊网Commonly, Principal Component Analysis (PCA) is used to remove the correlation.
一般用主成分分析去除波段之间的相关性。
参考来源 - 基于独立分量分析的遥感图像分类技术The content includes in the fuzzy segment,BP neural networks and principal component analysis.
内容涉及模糊分割技术、BP神经网络、主元分析技术。
参考来源 - 基于神经网络的目标识别及定位方法的研究The main difference between ICA and PCA(Principal Component Analysis)or SVD(Singular Value Decomposition) is that the components decomposed by the later method are only mutually uncorrelated,whereas the components decomposed by the former method are mutually independent statisically.
ICA与 PCA(主分量分析 )或 SVD(奇异值分解 )的主要不同是 :后者分解得的各分量只是互不相关 ,而前者则要求各分量相互统计独立。
参考来源 - 独立分量分析及其在生物医学工程中的应用·2,447,543篇论文数据，部分数据来源于NoteExpress
The principal component analysis implements the column-wise compression, using the correlation between attributes.
主成分分析使用属性之间的相关性实现按列压缩。
As the principal component in glass, silicon dioxide was used as early as 5000 BC.
作为玻璃的主要成分，二氧化硅早在公元前5000年就被使用。
Methods of principal component analysis, grey incidence analysis were employed.
研究方法：主成分分析法、灰关联分析。
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