matrix factorization中文是什么意思

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中文翻譯
造句

矩陣因子分解

matrix n. (pl. matrices 或matrixes) ...
matrix factorization method 矩陣因子分解方法
factorization 分解為因子; 因式分解; 因數分解; 因素化; 因子分解編制計算程序; 因子分解法
canonical factorization 典范因子分解
coupled factorization 耦合分解
factoring factorization 因式分解
factorization method 因子分解法; 因子分解方法
factorization of a transformation 變換的因式分解; 變換因式分解; 變換因子分解
factorization of polynomial 多項式因子分解
factorization technique 因子分解技術, 子分解技巧
factorization theorem 因子分解定理
integer factorization 整數分解; 質因數分解
lu factorization lulu分解法
optimum factorization 最佳因子分解法; 最優因子分解
polynomial factorization 多項式因式分解
prime factorization 標準分解式，分解成質數的乘績; 素因子分解
rank factorization 秩分解
spectral factorization 譜分解; 譜因式分解; 譜因子分解
triangular factorization 三角分解
unique factorization 唯一析因
a roximate factorization method 近似因子分解法
approximate factorization method 近似因子分解法
direct factorization of algebra 代數的直分解
factorization of algebraic equations 代數方程的因式分解
incomplete block factorization 不完全塊分解

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例句與用法

Multivariable robust adaptive backstepping control using matrix factorization
基于矩陣分解的多變量魯棒自適應反推控制
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
Secondly , we utilize the nmf ( non - negative matrix factorization ) algorithm to extract human face local feature subspace
然后,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征子空間。
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 )
首先通過主元分析算法( pca )提取全局特征,利用帶稀疏限制的非負矩陣分解算法( nmfs )提取局部特征。
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
本文主要采用snmf (非負稀疏矩陣分解)算法作為降維和提取特征向量的工具,該算法是在nmf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。
The traditional methods are to solve the linear algebra equations directly , based on matrix factorization such as lu decomposition . with this kind of methods , the " true " solution can be derived if there is no consideration of the round error
解線性代數方程組的傳統方法是利用lu分解等直接求解，雖然傳統方法具有理論上直接得到真解的優點，但當系數矩陣條件數很大時，存在嚴重的穩定性問題。
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
在眾多的特征提取算法中,基于全局特征提取的主元成分分析( principlecomponentanalysis , pca )是討論最多的經典算法,與此對應的是基于局部特征提取的非負矩陣分解( non - negativematrixfactorization , nmf )算法。
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
本文提出了一種基于非負矩陣稀疏分解( non - negativematrixfactorizationwithsparsenessconstraints , nmfs )和rbf神經網絡的人臉識別方法。通過控制稀疏度, nmfs算法既可提取人臉全局也能提取局部特征,再運用rbf神經網絡進行模式分類。
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)等降維算法不同, nmf (非負矩陣分解)算法能夠分解出非負的,稀疏的特征矩陣和編碼矩陣,能夠提取原始數據向量的局部特征,使基于局部特征進行分類的聚類算法更容易實現。

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