矩阵分解

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

以基于矩阵分解（MatrixFactorization）模型的协同过滤推荐技术为基础，提出了一种基于图约束的矩阵分解模型。

Based on the basic matrix factorization ( MF ) model , a graph-regularized matrix factorization model is proposed .

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 .

介绍了一种用于求解一维含时薛定谔方程的MATLAB矩阵分解算法。

A MATLAB matrix decomposition algorithm for solving the one-dimensional time-dependent Schr ? dinger equation is presented .

与经典的矩阵分解方法不同，本文以线性矩阵不等式方法，研究了激光导向伺服系统的满意PID调节器设计问题。

As is different from the classical matrix decomposition approach , linear marix inequality method is used to solve the problem of design of satisfactory PID controllers in the laser oriented servo system .

奇异值分解（SVD）是线性代数中一种重要的矩阵分解技术，一种对数据进行降维处理的方法。

Singular value decomposition ( SVD ) is a very important matrix decomposition technique , and a dimension reduction method in linear algebra .

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 .

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 .

综述了国内外用于可吸入颗粒物源解析的受体模型的最新研究进展，详细介绍了粗集理论模型、BP网络权重分析、投影寻踪回归、正定矩阵分解和主成分分析等多种方法。

The latest research progress of receptor models for source apportionment of inhalable particles is summarized . Rough Sets , BP network , projection pursuit regression , positive matrix factorization and principal component analysis are introduced in details .

采用矩阵分解方法，推导出一种求解广义预测控制（GPC）中的逆矩阵的递推算法，使得计算量大为减少。

A recursive algorithm of evaluating the invert-matrix of generalized predictive control ( GPC ), based the matrix decomposition , is introduced . The calculation has greatly been reduced .

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 .

计算机模拟结果表明，矩阵分解法是一种优于Bartlett法的高分辨力方法。

The results of computer simulation indicate that it is one kind of high resolution methods and better than Bartlett method .

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 .

使用矩阵分解技术，提出了混合进制广义Walsh函数的一种新的复制方法，设计了混合进制Walsh函数阵的两种快速算法。

By using the techniques of matrix analysis , a new copy method of . generalized Walsh function for hybrid carry system is proposed and two fast algorithms are designed in the paper .

一种基于Q-R矩阵分解的自适应滤波算法

An Adaptive Filtering Algorithm Based on Q-R Matrix Decomposition

根据显微镜景深短、视场小、标定板只能与CCD成像面平行放置等特点，提出了一种基于单视图单应矩阵分解的标定算法。

Aiming at the short depth of field and small field of view of microscopes , and installation of the calibration plate in parallel with the CCD image plane , an improved camera calibration method based on the single view homography matrix decomposition is proposed .

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。

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 ) .

SDD（半离散矩阵分解）算法是潜在语义索引（LSI）的最新技术，弥补了传统SVD算法无法大规模应用的局限，具有压缩比大，响应时间短等优点。

Semi-discrete decomposition of matrix is the latest technology of LSI , which improves the SVD and can be use in large scale . It has great compress rate , short response time .

基于单元级矩阵分解的EBE-PCG算法及其在网络机群并行环境上的实现

An EBE-PCG Parallel Algorithm with the Preconditioning Technique Based on the Matrix Cholesky Factorization at Element Level

在此基础上，借鉴量化DCT的思想，进一步将可分的量化矩阵分解为多个向量矩阵的组合与乘积，提高了图像第1次编解码后的图像质量。

Based on it , the separable quantization matrix is factorized into the combination and product of several vector matrices by making use of the idea of quantized-DCT , so as to improve the image quality of the first generation further .

提出了利用小波变换（WT）、非负稀疏矩阵分解（NMFs）和Fisher线性判别（FLD）来进行人脸识别。

This paper combines Wavelet Transformation ( WT ), Non-negative Matrix Factorization with sparseness constraints ( NMFs ), and Fisher 's Linear Discriminant ( FLD ) to extract features for face recognition .

实验结果展示了该方法的可行性。3）探讨了非负矩阵分解（NMF）算法的原理及在人脸识别中的应用。

The experimental results show that the recognition method is feasible . ( 3 ) The non-negative matrix factorization ( NMF ) algorithm and its application in face recognition is discussed .

与传统基于KL变换的算法相比，本论文提出的压缩算法既可以实现有损压缩也可以实现无损压缩，而且以全局选主元的矩阵分解算法实现整型变换，运算简单。

Compared with conventional KLT-based compression algorithms , the proposed method can realize both lossy compression and lossless compression . Besides , reversible integer transform is realized using quasi-complete pivoting matrix factorization method and low computational complexity . 2 .

而非负矩阵分解算法是一种对高维矩阵进行降维的方法，具有实现简单、可解释性强等优点，从而可以把NMF算法应用到矩阵降维中。

Non negative matrix factorization algorithm is a kind of high order matrix dimensionality reduction method , has a simple , interpretable advantages , which can keep the NMF algorithm is applied to the matrix dimensionality reduction .

常用的方法是借助于一些矩阵分解，如广义奇异值分解（GSVD），典型相关分解（CCD），和商奇异值分解（QSVD）等。

The techniques commonly used are some matrix-factorizations such as the generalized singular value decomposition ( GSVD ), the canonical correlation decomposition ( CCD ), and the quotient singular value decomposition ( QSVD ) .

然后，研究了基于非负矩阵分解（NMF）算法和奇异值分解（SVD）的信号特征提取，并利用稀疏约束条件下的NMF算法，进一步降低了特征信号的复杂度。

Then we extracted the signal characteristics base on the non-negative matrix factorization ( NMF ) algorithm and singular value decomposition ( SVD ), and improved sparse constraints NMF algorithm to further reduce the complexity of the characteristics of the signal .

阐述了二维快速余弦逆变换（IFCT），对用矩阵分解方法实现IFCT进行了分析，并给出了有助于编程实现的研究实例。

After explanation of the inverse fast cosine transform ( IFCT ) of two dimensions system , a method of performing IFCT is analyzed by means of the matrix analysis , furthermore , an useful example for programming performance is deduced too .

并使用该系统对内蒙古东部地区1：20万银矿产资源进行了定量预测，利用选定的局部非负矩阵分解方法（LNMF）对集成变量矩阵中的主要特征向量进行了提取与处理。

This system is employed to quantitatively predict 1:200,000 scale silver mineral prediction in the east of Inner Mongolia . Main features of the matrix vectors are extracted by using the selected local non-negative matrix factorization method ( LNMF ) .

第一种是受限非负矩阵分解法。

One is the method based on constrained non-negative matrix factorization .

基于非负矩阵分解和广义判别分析的掌纹识别

Palmprint Recognition Based on Non-Negative Matrix Factorization and General Discriminant Analysis