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negative matrix中文是什么意思

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用"negative matrix"造句"negative matrix"怎么读"negative matrix" in a sentence

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  • 负矩阵

例句与用法

  • The numerical range of non - negative matrix
    非负矩阵的数值域
  • Negative matrices over a c - algebra and geometric mean of two positive definite elements
    代数上的非负矩阵与正定元的几何平均
  • 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
  • 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。
  • 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。
  • In chapter 4 , we research a way how to generate curve by discrete points . the way uses several low - order bezier curve to substitute one high - order bezier curve in order to avoid computing high - order negative matrix . the curve produced by this way has good smoothness
    第四章对由离散点构建曲线进行了研究,绕过高阶矩阵求逆,把由k个低阶的bezier曲线拼合产生的复合曲线来代替单一的高阶bezier曲线,并保证曲线之间的光滑过渡。
  • 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。
  • 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。
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