非矩阵

ABLE方法可以用于同时求解大规模非对称矩阵的特征值及其相应的左、右特征向量。

The ABLE method can be used to compute eigentriplets of large scale non-Hermitian matrices .

将非负矩阵分解和NormalMatrix谱分解理论应用于肿瘤基因表达谱数据的分类上。

Apply nonnegative matrix decomposition and Normal_Matrix spectrum decomposition theory to the classification of gene expression data .

确定实非对称矩阵Jordan标准型的一种算法

An algorithm for determining Jordan canonical form of a real Unsymmetric Matrix

基于快速非负矩阵分解和RBF网络的高光谱图像分类算法

A Hyper-spectral Image Classification Algorithms Based on Quick Non-negative Matrix Factorization and RBF Neural Network

在本章，我们给出了非负矩阵为树结构逆M、逆Z矩阵的充分条件以及充要条件。

Sufficient and necessary conditions for a matrix to be an inverse M-matrix and inverse Z-matrix are given .

使用非负矩阵分解方法识别脸部表情基于人脸局部特征和SVM的表情识别

Recognizing Facial Expression with Non-negative Matrix Factorization A Face Expression Recognition Method Based on Face Features and Support Vector Machine

该方法首先将数字图像的非负矩阵表示转化为G-对角占优矩阵，再进行LU分解，通过量化函数进行数字水印的嵌入，恢复水印时不需要原始图像。

In order to apply LU decomposition , the corresponding nonnegative matrix of the image is transformed into G-diagonally dominant matrix .

非负矩阵分解算法（Non-negativeMatrixFactorization，简称NMF）是目前国际上提出的一种新的矩阵分解方法。

Non-negative Matrix Factorization ( NMF ) is one of the recently emerged dimensionality reduction methods .

LU分解是一种将非奇异矩阵进行三角分解的方法，而数字图像也可以看作矩阵。

LU decomposition is a triangular decomposition approach of non-singular matrix , and the digital image can be seen as a matrix .

非负矩阵因子分解（non-negativeMatrixFactorization，NMF）是对非负数据处理的一种多元统计分析方法。

Non-negative matrix factorization ( NMF ) has been proposed for multivariate data analysis , with non-negativity constraints .

Hermitian非自反矩阵的两类反问题

Two Kinds of Inverse Problems for Hermitian Anti-reflexive Matrices

基于Lanczos双对角化过程的非负矩阵快速分解的初始化方法

Lanczos Bidiagonalization Based Initialization : A Fast Head Start for Nonnegative Matrix Factorization

由Cmxn中矩阵F范数的酉不变性质，给出了实对称矩阵与实非奇异矩阵F范数的特征性质及其推论

According to the F norm 's unitary invariant property of C mxn , the characteristic properties and deductions of the real symmetrical matrix and nonsingular matrix 's F norm are achieved

逆M矩阵和逆Z矩阵是重要的非负矩阵且有着广泛的应用，特别是生物学、物理学和数学中的很多问题都与二者理论有着密切的关系。

Inverse M-matrices and Inverse Z-matrices are important matrix and there are many application involving inverse M-matrices and inverse Z-matrices , especially in biology , physics and mathematics .

NMF寻找两个小的非负矩阵，使得它们的乘积能够最好的拟合原始矩阵。

NMF seeks for two non-negative matrices whose product can best approximate the original matrix .

基于非负矩阵理论的同步网络AIMD算法分析

Analysis of AIMD Algorithms in Synchronized Networks Based on Nonnegative Matrices Theory

非负矩阵的Perron补与Perron根的估计

The Perron Complement of Nonnegative Matrices and Estimation of Perron Roots

详细介绍了NMF（非负矩阵分解）相关反馈应用到医学图像检索。

Details the applications of nonnegative matrix factorization ( NMF ) relevance feedback to medical image retrieval .

非负矩阵因子分解算法解析手性药物重叠峰的HPLC-DAD数据

Resolution of HPLC-DAD data of chiral drugs with overlapping peaks by non-negative matrix factorization

非负矩阵Drazin逆的非负性

Nonnegative Drazin Inverses of a Nonnegative Matrix

最后，将子空间分解的思想引入非负矩阵分解法（NMF，NonnegativeMatrixFactorization）用以解决单通道乐音信号盲分离问题。

Finally , this paper combines nonnegative matrix factorization ( NMF ) with subspace decomposition theory to solve the problem of single channel music source blind separation .

计算非对称矩阵广义特征值问题的组合QZ法

The Combination QZ Algorithm for the Nonsymmetric Generalized Eigenvalue Problem

设A，B为两个n×n可对称化矩阵，即存在非奇异矩阵P，Q使得此处均为实数，本文证明了：其中表示谱范数，而表示Frorbenius范数。

Let A and B be two n × n symmetrizable matrices , i. e. , there exists nonsingular matrices P , Q such that where are all real , and . It is proved that where denote the spectral norm ;

完全非负矩阵上的Oppenheim不等式的推广

The Generalization of Oppenheim 's Inequalities for Totally Nonnegative Matrices

非负矩阵Perron根的下界序列

Lower Bounds Sequence of Nonnegative Matrix Perron roots

并进一步研究了非负矩阵Perron余的有关性质，如对角占优矩阵及H矩阵的Perron余的对角占优性，以及广义双对优矩阵的Perron余的有关性质等。

We also analyze the relative features of perron complement , H-matrix , diagonally dominant matrix , and generalized double diagonally dominant matrix .

事实也确实如此，有序抽样样本下的Fisher信息矩阵可表示为SRS样本下的Fisher信息矩阵和一个非负矩阵之和。

It is proved that the Fisher information matrix from an RSS sample is the sum of the information matrix from an SRS sample and an additional non-negative definite matrix .

提出了一种基于非负矩阵分解（NMF）和隐马尔可夫模型（HMM）的人体行为识别的方法。

A method based on Nonnegative Matrix Factorization ( NMF ) and Hidden Markov Model ( HMM ) is proposed in this paper for human behavior recognition .

但理论上在估计相关源信号时，该算法只能估计上下界，不能做出精确估计；第二种是基于非负矩阵分解（Non-negativeMatrixFactorization：NMF）的源数估计法。

However , the round of the number of correlated sources can be estimated theoretically , and the exact value can not be got . The second is the method based on non-negative matrix factorization ( NMF ) .

首先，在利用非负矩阵分解进行过程信息挖掘的基础上建立了非负成分回归（NCR）模型。

First , a Non-negative Component Regression model is developed based on the features which are extracted from process information by Non-negative Matrix Factorization .