first principal component 中文意思是什麼
first principal component
解釋
第一主成分-
First - generation computers were characterized by the use of vacuum tubes as their principal electronic component
第一代計算機的特色是使用真空管為其主要電子器件。 -
The conventional principal component analysis ( pca ) and fisher linear discriminant analysis ( lda ) are based on vectors. that is to say, if we use them to deal with the image recognition problem, the first step is to transform original image matrices into same dimensional vectors, and then rely on these vectors to evaluate the covariance matrix and to determine the projector
所提出的這兩種方法的共同特點是，在進行圖像特徵抽取時，不需要事先將圖像矩陣轉化為高維的圖像向量，而是直接利用圖像矩陣本身構造圖像散布矩陣，然後基於這些散布矩陣進行主分量分析與線性鑒別分析。 -
First principal component
第一主成分 -
At the first step of the algorithm, based on the theory of principal component analysis, a modified principal component analysis ( mpca ) approach is adopted to reduce the dimension of data horizontally, then after comparing existing clustering algorithms, an immune clustering algorithm is put forward based on similarity measurement of principle component core for vertical reduction by using related mechanism of clone selection as well as immune network self - stabilization in natural organic immune system for reference
在進行樣本參量的降維處理時，基於主元分析的思想，採用一種改進的主元分析（ mpca ）方法用於橫向數據壓縮，在壓縮樣本數量時，綜述和比較了現有的各種聚類演算法，借鑒生物體自然免疫系統中克隆選擇以及免疫網路自穩定等有關機理，提出了基於主元核相似度的免疫聚類演算法用於縱向數據壓縮。 -
Main characteristic and innovative points : 1. the sensitivity analysis is first to applied in the reaction mechanism reduction of lpg combustion under premixed lean burn, that is, finding a reduced mechanism that only relates the principal component and skeletal mechanism but can produces similar predictions of detailed mechanism
本文主要特色及創新之處如下： 1運用chemkin對化學反應機理進行簡化的能力，首次將敏感度分析法用於lpg在稀燃條件下化學機理的簡化，確定關鍵組分和主要反應，並把這個簡化機理應用到star - cd中進行計算。 -
At first, i developed the principle and restrictive factor to optimize the investment structure of regional industry ; secondly, i applied the grey theory to research the relationship between structure of investment and structure of industry, the relationship between structure of industry and economy of region. ; thirdly, three methods, including ahp, principal component analysis and relative potential were exerted to research the order of investment afterward, i developed qualitative analysis to the third industry, and put forward suggest to its progress direction ; fourthly, using economics and operational research ' s thoughtway for reference, i upbuilt the dynamic model of investment portion model. at last, according as the regional concrete situation and market environment of wto transition period, i developed some policy suggests, in order ensuring the optimization of investment structure to be realized successfully
首先，提出了優化區域產業投資結構的原則和制約因素；其次，運用灰色系統理論對區域的產業結構與整體經濟、投資結構與產業結構的關系進行論證分析；第三，建立了區域產業投資結構評價指標體系，並運用ahp法、主成份分析法和相對勢法對河北省第一、第二產業投資排序進行了研究，然後，在充分利用現有數據的前提下對第三產業進行了定性的分析，並對第三產業的發展方向提出了建議；第四，借鑒了經濟學、管理學、運籌學等一些理論的思想方法，建立了區域產業連續投資分配比例模型；最後，根據河北省情，結合中國進入wto的過渡期市場環境，提出了一些政策建議，以達到實現河北省產業投資結構優化的目的。 -
According to present situation of brand equity, for the first time, five important brand equity factors were extracted from brand features by applications of principal component analysis and factor analysis methods, they were brand status, customer - recognized value, brand image, brand creative abilities and brand executive abilities ; on the same time, five types of brand equity were divided with k - means cluster methods on the base of five brand factors, they were leading brand, matured or ripe brand, concrete brand, customer - based brand and creative brand. in order to extract brand equity strategy, correlation and linear regression analysis methods were used, as a result of analysis, four strategies were put forwarded including brand marketing strategy, marketing dividing strategy, marketing stretching strategy and marketing entrance time, applying nonparametric tests and duncan tests, five brand equities were also differed in many aspects
在品牌資產各組成要素中，應用主成分分析和因子分析方法，提取了五個品牌資產最重要的構成因子，首次提出品牌資產最重要的因子是品牌地位和顧客認知價值，其次為品牌形象、品牌創新能力和市場執行能力；根據品牌資產的構成因子，運用聚類分析法，對調查企業的品牌資產類型進行了分類，按照品牌構成屬性將企業分為領導型、成熟型、務實型、顧客導向型和創新型品牌企業；在對企業品牌策略分析基礎上，運用相關分析和線形回歸方法，求導形成品牌的重要策略因子，提出建立品牌資產最重要的策略因子是推廣策略，其次為市場分化策略、市場延伸策略和進入市場時機。 -
Via numeric sample analysis, it is found that evaluation functions are constructed by weighing principal components for pca. however, evaluation functions can be quite different when there are more than three principal components and characteristic vectors other than first one are chosen in different directions. for spca, variance contribution can be greater than 90 % by selecting just one principle component
將譜主成分分析應用於多指標評價系統中，通過數值例子分析：主成分分析是通過對各個主成分加權構造評價函數，當主成分個數不小三個時，從第二個特徵向量開始，對方向的不同選取，可導致評價函數的極大差異：而用譜主成分分析，能做到只取一個譜主成分就可使方差貢獻率大於90 。 -
Therefore, spca gotten via selecting polynomial kernel functions is more accurate than pca in multi - index evaluation system, and has fewer dimensions. comparatively, for spca using gauss kernel function and laplace kernel function, it is required to normalize original data according to the category, and the constructed evaluation functions are better than the ones constructed via using pca. however, if variance contribution of first principal component of pca is more than 85 %, evaluation function curves of spca and pca are similar
在作多指標評價中，選用多項式核函數而得到的譜主成分分析，比主成分分析得到的主成分具有維數低且精度高的優點；而用gauss核函數和laplace核函數的譜主成分分析，需對原數據作同類別數據間的規范化，其構造的評價函數也優于用主成分方法構造的評價函數。 -
First pre - process the face image and localize the eyes, then normalize the face image based on the location of eyes, and extract the face feather using the independent component analysis algorithm combined with the principal component analysis algorithm to reduce the face image dimension ; attempt a direct gray scale minutiae detection approach based on ridgeline following to extract fingerprint feather, it will overcome the shortcoming of traditional method which would bring in false feathers during the binarization and thinning, and make some improvement such as the fingerprint image enhancement, direction field, stop criteria of ending point
首先對人臉圖像進行預處理、定位人眼位置，進而根據人眼位置進行人臉圖像的幾何歸一化，對歸一化后的人臉圖像本文提出了ica與pca相結合的方法提取人臉圖像的特徵:首先，利用主元分析法（ pca ）對人臉圖像數據進行降維，然後採用獨立分量分析（ ica ）演算法提取人臉整體特徵，從而得到較準確的人臉特徵。 -
This new approach first computes the motion vectors between a tcst face image and a neutral training image using the block - matching metbod, tben projects the motion vectors to a low dimensional subspace that is pre - trained by applying principal component analysis ( pca ) to motion vectors resulting from training imagcs with expression variations. this subspace is called an eigenmotion space
為了提高表情人臉識別的魯棒性，該方法首先利用塊匹配的方法來確定表情人臉和無表情人臉之間的運動向量，然後利用主成分分析方法（ pca ）從這些運動向量中產生低維子空間，稱之為特徵運動空間。
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