非負矩陣 的英文怎麼說

中文拼音 [fēifùjǔzhèn]
非負矩陣 英文
nonnegative matrix
  • 非 : Ⅰ名詞1 （錯誤） mistake; wrong; errors 2 （指非洲） short for africa 3 （姓氏） a surname Ⅱ動詞1 ...
  • 負 : Ⅰ名詞1 （負擔） burden; load 2 （虧損） loss 3 （失敗） defeat Ⅱ動詞1 [書面語] （背） carry on th...
  • 矩 : 名詞1. （畫直角或正方形、矩形用的曲尺） carpenter's square; square2. （法度; 規則） rules; regulations 3. [物理學] moment
  • 陣 : Ⅰ名詞1 （作戰隊伍的行列或組合方式） battle array [formation]: 布陣 deploy the troops in battle fo...
  • 矩陣 : [數學] matrix; array
  1. Negative matrices over a c - algebra and geometric mean of two positive definite elements

    代數上的非負矩陣與正定元的幾何平均
  2. Further discussion on nonnegative matrix having same nonnegative moore - penrose and group inverse

    逆的非負矩陣的進一步研究
  3. We make the following assumption for when 2 is positive definite matrix, different estimators about matrix of regression coefficients and inefficiency of least squares estimate have been discussed in many documents. considered 2 is nonnegative definite matrix, this thesis derives best linear unbiased estimate of parameter matrix b and estimable parameter function kbl under the meaning of matrix nonnegative definite and the property of maximum probability of blue is investigated. next, we discuss some necessary and sufficient conditions of the equality of the lse and blue, then we derive the estimation of the deviation bet - ween the least squares and the best linear unbias estimators of the mean matrix, meanwhile a relative efficiency of lse ofb is proposed and its bound is given

    當0時，眾多文獻討論了回歸系數陣的各種估計及lse的有效性，本文考慮了當0的情形，給出了回歸系數陣b及其可估參數函數kbl的在矩陣非負定意義下的最優估計（ blue ） ，研究了它的一個最大概率性質，並且討論了最小二乘估計成為最佳線性無偏估計的充分必要條件，在此基礎上給出了均值矩陣的最小二乘估計與blue的偏差估計，定義了lse相對于blue的一個相對效率，並給出了它的界。
  4. Also, by the means of the pattern of matrix and the pattern of linear operator, we characterize the linear operators that strongly preserve nilpotence and that strongly preserve invertibility over antinegative commutative semirings without zero divisors

    另外，利用矩陣模式和運算元模式等工具，我們在非負無零因子半環上刻畫了強保持冪零的線性運算元和強保持可逆的線性運算元
  5. A new estimation for a spectral radius of nonnegative matrices

    非負矩陣譜半徑的新估計
  6. Notes on bounds for spectral radius of a nonnegative matrix

    關于非負矩陣譜半徑界的注記
  7. The numerical range of non - negative matrix

    非負矩陣的數值域
  8. Estimation of new bounds on the spectral radius of nonnegative matrix

    非負矩陣譜半徑的一個新界值估計
  9. Bounds on the spectral radius and the spread of nonnegative matrix

    有關非負矩陣譜半徑及分離度界的估計
  10. Those main proofs of this part are based on nonnegative

    這一部分結果的論證建立在非負矩陣的理論基礎上。
  11. Non - negative matrix factorization and its applications to gene expression data analysis

    非負矩陣分解及其在基因表達數據分析中的應用
  12. A doubly nonnegative matrix a is defined to be an entrywise nonnegative and positive semidifined matrix

    對于給定的一個n階實方陣a ，若其每一元素非負且半正定，則稱為雙非負矩陣。
  13. Different from other rank reduction methods, such as pca ( principal component analysis ) and vq ( vector quantization ), nmf ( nonnegative matrix factorization ) can get nonnegative, sparse basis vectors which make possible of the concept of a parts - based representation

    與pca （主分量分析）和vq （矢量量化）等降維演算法不同， nmf （非負矩陣分解）演算法能夠分解出非負的，稀疏的特徵矩陣和編碼矩陣，能夠提取原始數據向量的局部特徵，使基於局部特徵進行分類的聚類演算法更容易實現。
  14. The holistic features are extracted by principal component analysis ( pca ), and the local features are extracted by non - negative matrix factorization with sparseness constraints ( nmfs )

    首先通過主元分析演算法（ pca ）提取全局特徵，利用帶稀疏限制的非負矩陣分解演算法（ nmfs ）提取局部特徵。
  15. In this thesis, we mainly use snmf ( sparse nonnegative matrix factorization ) as the method of rank reduction, which extend the nmf to include the option to control sparseness explicitly

    本文主要採用snmf （非負稀疏矩陣分解）演算法作為降維和提取特徵向量的工具，該演算法是在nmf演算法的基礎上加上顯式地稀疏因子控制而形成的一種非負矩陣分解方法。
  16. Some conditions are obtained by using the semigroup theory, the properties of nonnegative matrices and the techniques of inequalities to determine the asymptotically stable region of the equilibrium

    通過半群理論、非負矩陣性質和不等式技巧，得到估計這類方程平衡態漸近穩定域的方法。
  17. In [ 3 ], z. s. li, f. hall and c. eschenbash extended the concept of the base and period from nonnegative matrices to powerful sign patter matrices

    文[ 3 ]中，李宗山等把非負矩陣的基和周期的概念推廣到powerful符號矩陣。
  18. Secondly, we utilize the nmf ( non - negative matrix factorization ) algorithm to extract human face local feature subspace

    然後，對獲得的類人臉膚色區域利用nmf （ non - negativematrixfactorization ）非負矩陣分解的方法提取人臉局部特徵子空間。
  19. Principle component analysis ( pca ), as a classical method for feature extraction, learns holistic representations of facial images, while non - negative matrix factorization ( nmf ), a recently proposed approach, learns parts - based representations of faces. however, we argue that nmf can not only learn parts - based representations but also holistic ones with different sparseness constraints

    在眾多的特徵提取演算法中，基於全局特徵提取的主元成分分析（ principlecomponentanalysis ， pca ）是討論最多的經典演算法，與此對應的是基於局部特徵提取的非負矩陣分解（ non - negativematrixfactorization ， nmf ）演算法。
  20. In this thesis, we propose an efficient nmfs + rbf aggregate framework for fr, in which non - negative matrix factorization with sparseness constraints ( nmfs ) is firstly applied to learn either the holistic representations or the parts - based ones by constraining the sparseness of the basis images, and then the rbf classifier is adopted for pattern classification

    本文提出了一種基於非負矩陣稀疏分解（ non - negativematrixfactorizationwithsparsenessconstraints ， nmfs ）和rbf神經網路的人臉識別方法。通過控制稀疏度， nmfs演算法既可提取人臉全局也能提取局部特徵，再運用rbf神經網路進行模式分類。
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