[自][數] 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.
利用主成分分析對以綠頭野鴨為父本的不同雜交組合的屠宰性能進行了研究。
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，區系地理成分的對比又包括：植物區系譜系，主成分分析（PCA）和聚類圖分析；
Principal component analysis (PCA) and correlative component analysis (CCA) are commonly used techniques to extract components.
主成分分析和相關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.
線性主成分分析是一種線性分析方法，而數據通常是非線性的。
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）對山茶油與大豆油及其混合物檢測效果較差，對芝麻油、大豆油及兩者混合物取得了較好的檢測效果；
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.
本文通過對亞洲群體體部測量資料的聚類分析與主成分分析，認為亞洲群體可分為北方類群與南方類群。
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歸分析了影響土壤重金屬的有效性及其微生物學效應的因素。
Principal component analysis indicated that panicle length factor had very significantly positive linear relationship to yield.
主成分分析表明：穗長因子對産量有極顯著正線性關系；
Methods The methods of analysis were used by expert consulting graded approach , discrete tendency , principal component analysis and cluster analysis.
方法采用專家咨詢打分法、離散趨勢法、主成分分析法和聚類分析法進行分析。
One way to improve the robustness of principal component analysis is stu***d in order to increase the accuracy of PCA algorithm.
夏紹玮，王松主要研究了改善主成分分析算法魯棒性的一種實現途徑，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.
以收獲期10項指标的相對抗旱系數為基礎，應用主成分分析方法對黑龍江省202份大豆基因型進行抗旱性評價研究。
Through principal component analysis, we have synthesized numerous indexes, (eliminated) information overlapping of the sample, and reduced the input dimension of BP network.
通過主成分分析法将衆多指标進行綜合，消除樣本間的信息重疊，降低BP網絡的輸入維數。
It can obtain better result by the way of constellation graphics than by the way of combining principal component analysis with fuzzy clustering method.
采用星座圖法對森林景觀生态功能進行分區比利用主成分分析與模糊聚類相結合方法能得到更為滿意的結果。
Then, this paper synthetically evaluates the comprehensive strength of patent about more developed 10 areas of our country by using the principal component analysis.
在此基礎上，運用主成分分析方法對我國10個經濟較發達地區的專利實力進行了分析評價。
Then the basic concepts of principal component analysis and partial least square regression are stu***d, as well as their advantages and disadvantages.
引入了主成分分析與偏最小二乘回歸等多元統計與回歸方法，并分析了其基本思想與優缺點。
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.
首先對曆史光譜數據進行主成分分析（PCA）處理，再根據PCA特征數據與其表面溫度的對應關系建立溫度的估計模型，該模型是基于高斯核函數的。
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，從多種影響因素中找出幾個主要成分指标，較為全面地綜合評價了學生素質。
The principal component analysis(PCA) approach for sensor fault detection, identification and reconstruction in HVAC system is presented.
提出了空調系統傳感器故障檢測、故障識别、故障重構的主成分分析方法。
Methods We used principal component analysis and factor analysis to evaluate the 6 hospital's management quality.
方法用主成分分析與因子分析法對6所醫院的管理工作質量進行綜合評價。
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.
多元統計分析的主成份分析方法是對多維矢量數據提取主要特征分量，以此達到壓縮矢量維數的目的。
Conclusion Practice presents that principal component analysis and factor analysis both are suitable for synthetic evaluation of hospital management quality.
結論理論和實踐表明主成分分析與因子分析法適宜于客觀地綜合評價醫院管理工作質量。
Competitive power evaluation index system of towns in Shijiazhuang city is constructed by using principal component analysis method.
針對石家莊市域城鎮發展現狀，利用主成分分析方法，構建了競争力評價指标體系。
To improve the running efficiency of the algorithm, the stochastic mapping of the patterns was modified based on principal component analysis.
為了提高群體智能聚類算法的運行效率，提出了利用主成分分析改善模式投影時的隨機性。
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）的人臉識别算法能夠提取非線性圖像特征，在小樣本訓練條件下有較好性能。
主成分分析（Principal Component Analysis，PCA）是一種基于統計學的無監督降維方法，其核心目标是将高維數據轉換為低維表示，同時保留數據的主要變化特征。該方法通過正交變換将可能存在相關性的原始變量轉換為線性無關的主成分，并按方差貢獻度從高到低排序。
數學原理 PCA的數學基礎是協方差矩陣的特征值分解。假設數據集$X$已标準化處理，協方差矩陣為： $$ C = frac{1}{n-1}X^T X $$ 求解該矩陣的特征方程$Cv = lambda v$，其中$lambda$為特征值，對應的特征向量$v$即為主成分方向。第$k$個主成分的方差貢獻率為$lambdak/sum{i=1}^p lambda_i$，通常選取累計貢獻率超過85%的前$m$個主成分。
核心特征
典型應用場景
優勢與局限 該方法計算複雜度為$O(p)$（$p$為特征維度），當樣本量遠大于特征數時效率較高，但需注意數據标準化預處理的重要性。對于非線性數據結構，建議結合核方法（Kernel PCA）使用。
來源參考：
主成分分析（Principal Component Analysis，PCA）是一種無監督的統計方法，主要用于數據降維和特征提取。其核心目标是通過線性變換将高維數據投影到低維空間，同時保留數據中的主要信息。
設數據矩陣(X)标準化後，協方差矩陣為： $$ C = frac{1}{n-1} X^T X $$ 通過特征分解(C = V Lambda V^T)，其中(Lambda)為特征值對角矩陣，(V)為特征向量矩陣。降維後的數據為： $$ Y = X V_k $$ 其中(V_k)為前(k)個特征向量組成的矩陣。
例如，若某數據集有100個特征，通過PCA發現前5個主成分解釋了95%的方差，則可僅用這5個維度進行後續分析，大幅簡化計算。
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