Chinese translation for "matrix factorization"

矩阵因子分解

Related Translations:

unique factorization theorem:  唯一析因定理

element matrix:  单元矩阵

behavioural matrix:  行为矩阵

strain matrix:  应变矩阵

rock matrix:  岩石基体

organic matrix:  有机基体

scrambling matrix:  密码矩阵

admittance matrix:  导纳矩阵

loxodromic matrix:  斜驶矩阵

difference matrix:  差分矩阵

Example Sentences:

	1.	Multivariable robust adaptive backstepping control using matrix factorization 基于矩阵分解的多变量鲁棒自适应反推控制
	2.	Non - negative matrix factorization and its applications to gene expression data analysis 非负矩阵分解及其在基因表达数据分析中的应用
	3.	Secondly , we utilize the nmf ( non - negative matrix factorization ) algorithm to extract human face local feature subspace 然后,对获得的类人脸肤色区域利用nmf ( non - negativematrixfactorization )非负矩阵分解的方法提取人脸局部特征子空间。
	4.	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 ) 首先通过主元分析算法( pca )提取全局特征,利用带稀疏限制的非负矩阵分解算法( nmfs )提取局部特征。
	5.	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 (非负稀疏矩阵分解)算法作为降维和提取特征向量的工具,该算法是在nmf算法的基础上加上显式地稀疏因子控制而形成的一种非负矩阵分解方法。
	6.	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 解线性代数方程组的传统方法是利用lu分解等直接求解，虽然传统方法具有理论上直接得到真解的优点，但当系数矩阵条件数很大时，存在严重的稳定性问题。
	7.	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 在众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 )是讨论最多的经典算法,与此对应的是基于局部特征提取的非负矩阵分解( non - negativematrixfactorization , nmf )算法。
	8.	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 本文提出了一种基于非负矩阵稀疏分解( non - negativematrixfactorizationwithsparsenessconstraints , nmfs )和rbf神经网络的人脸识别方法。通过控制稀疏度, nmfs算法既可提取人脸全局也能提取局部特征,再运用rbf神经网络进行模式分类。
	9.	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 与pca (主分量分析)和vq (矢量量化)等降维算法不同, nmf (非负矩阵分解)算法能够分解出非负的,稀疏的特征矩阵和编码矩阵,能够提取原始数据向量的局部特征,使基于局部特征进行分类的聚类算法更容易实现。

Similar Words：

"matrix encoder" Chinese translation, "matrix equality" Chinese translation, "matrix equation" Chinese translation, "matrix error" Chinese translation, "matrix exponential function" Chinese translation, "matrix factorization method" Chinese translation, "matrix fiber" Chinese translation, "matrix fibre" Chinese translation, "matrix film" Chinese translation, "matrix filter hybridization" Chinese translation