四元数矩阵的奇异值分解及其应用

In this paper, a constructive proof of singular value decomposition of quaternion matrix is given by using the complex representation and companion vector of quaternion matrix and the computational method is described. As an application of the singular value decomposition, the CS decomposition is proved and the canonical angles on subspaces of Q^n is studied.     

矩阵的奇异值分解的应用

本文通过引入矩阵奇异值的说明率及其模型考核的相关指标将矩阵的奇异值分解（ＳＶＤ）应用于我国育龄妇女接龄生育率数据的数学建模之中，得到了按龄生育率的可用于进行预测的按龄线性模型。     

求矩阵奇异值分解的一种新方法

旨在给出求矩阵奇异值分解的一种新方法.改进和克服了以往方法的缺陷和不足.     

矩阵奇异值分解理论在中文文本分类中的应用

用改进的截断与转换的矩阵奇异值分解算法,设计实现了基于字频特征的中文文本分类器.理论分析与实验结果表明,采用的方法提高了数值计算精度,降低了文本集特征空间的维数,简化了文本分类算法的时间复杂度,提高了文本分类准确率.     

用随机奇异值分解算法求解矩阵恢复问题

本文研究了大型低秩矩阵恢复问题.利用随机奇异值分解（RSVD）算法,对稀疏矩阵做奇异值分解.该算法与Lanczos方法相比,在误差精度一致的同时运算时间大大降低,且该算法对相对低秩矩阵也有效.     

分裂四元数矩阵的奇异值分解及应用

利用i-共轭重新定义了分裂四元数矩阵的共轭转置,在此基础上借助复表示和友向量研究了分裂四元数矩阵的奇异值分解,并利用所得结果解决了分裂四元数矩阵的极分解和分裂四元数矩阵方程AXB-CYD=E.     

酉延拓矩阵的奇异值分解及其广义逆

从普通奇异值分解出发,导出了酉延拓矩阵的奇异值和奇异向量与母矩阵的奇异值和奇异向量间的定量关系,同时对酉延拓矩阵的满秩分解及g逆,反射g逆,最小二乘g逆,最小范数g逆作了定量分析,得到了酉延拓矩阵的满秩分解矩阵F*和G*与母矩阵A的分解矩阵F和G之间的关系.最后给出了相应的快速求解算法,并举例说明该算法大大降低了分解的计算量和存储量,提高了计算效率.     

矩阵奇异值分解问题重分析的摄动法

本文提出了一般实矩阵奇异值分解问题重分析的摄动法.这是一种简捷、高效的快速重分析方法,对于提高各种需要反复进行矩阵奇异值分解的迭代分析问题的计算效率具有较重要的实用价值.文中导出了奇异值和左、右奇异向量的直到二阶摄动量的渐近估计算式.文末指出了将这种振动分析方法直接推广到一般复矩阵情况的途径.     

关于矩阵奇异值分解的注记

本文首先改进"具有奇异值分解性质的代数”一文的引理1及证明,再给出其定理i的简证,最后指出"关于‘体上矩阵的广义逆'一文的注”中一段话的错误.     

O-对称矩阵的奇异值分解及其算法

本文研究了具有轴对称结构矩阵的奇异值分解,找出了这类矩阵奇异值分解与其子阵奇异值分解之间的定量关系.利用这些定量关系给出这类矩阵奇异值分解和Moore-Penrose逆的算法,据此可极大地节省求该类矩阵奇异值分解和Moore-Penrose逆时的计算量和存储量.     

Singular Value Decomposition and Its Visualization

Singular value decomposition (SVD) is a useful tool in functional data analysis (FDA). Compared to principal component analysis (PCA), SVD is more fundamental, because SVD simultaneously provides the PCAs in both row and column spaces. We compare SVD and PCA from the FDA view point, and extend the usual SVD to variations by considering different centerings. A generalized scree plot is proposed to select an appropriate centering in practice. Several useful matrix views of the SVD components are introduced to explore different features in data, including SVD surface plots, image plots, curve movies, and rotation movies. These methods visualize both column and row information of a two-way matrix simultaneously, relate the matrix to relevant curves, show local variations, and highlight interactions between columns and rows. Several toy examples are designed to compare the different variations of SVD, and real data examples are used to illustrate the usefulness of the visualization methods.     

生长曲线模型中基于奇异值分解的岭估计

在生长曲线模型中将设计阵的奇异值分解与普通的岭估计相结合,针对设计阵A与C至少有一个病态时的情况提出生长曲线模型中基于奇异值分解的岭估计.比较其在均方误差,均方误差矩阵,及PC准则下相对于最小二乘估计的优良性.证明其容许性并利用Hemmerle和Brantle用于确定广义岭估计参数的方法给出极小化均方误差的无偏估计法选取岭参数.     

矩阵奇异值分解问题重分析的直接摄动法

矩阵奇异值分解的摄动重分析技术具有广泛的应用前景,作者继在文[2]中提出了一种间接摄动分析方法之后,在本文中又进一步提出了直接摄动法,建立了一般实矩阵的非重奇异值及其左、古奇异向量的二阶摄动计算公式.这可满足大多数实际应用问题的一般需要.文中以算例说明了直接摄动法的有效性.     

矩阵的同时加权(M,N)奇异值分解

A necessary and sufficient condition for the existence of simultaneous （M, N） singular value decomposition of matrices is given. Some properties about the weighted partial ordering are discussed with the help of the decomposition.     

基于奇异值分解和K means算法的近海水质数据分类方法

以辽东湾某生态监测区水质监测数据为例,以矩阵的奇异值分解和K means算法为分类工具,给出生态监测区水质监测数据的分类方法.方法具有以下特点:通过奇异值分解简化并加速了类比过程,通过动态设置类K避免了K means算法先设定类数的不足,还探讨了对少量新增监测数据的归类问题.方法对近海海水水质监测数据分类具有普适性.     

On the Cauchy Principal Value of the Khenkin-Ramirez Singular Integral in Strictly Pseudoconvex Domains of ℂ <Superscript>n</Superscript>

We show that in the multidimensional case (unlike the complex plane) the Cauchy principal value of the Khenkin-Ramirez singular integral in strictly pseudoconvex domains is equal to the limit value of this integral inside the domain.Original Russian Text Copyright © 2005 Kytmanov A. M. and Myslivets S. G.The first author was supported by a grant of the President of the Russian Federation and the State Maintenance Program for the Leading Scientific Schools of the Russian Federation (Grant NSh-1212.2003.1); the second author was supported by the Krasnoyarsk Region Science Foundation (Grant 12F0063C).__________Translated from Sibirskii Matematicheskii Zhurnal, Vol. 46, No. 3, pp. 625–633, May–June, 2005.     

Lanczos and the Riemannian SVD in information retrieval applications

Variations of the latent semantic indexing (LSI) method in information retrieval (IR) require the computation of singular subspaces associated with the k dominant singular values of a large m × n sparse matrix A, where k?min(m,n). The Riemannian SVD was recently generalized to low‐rank matrices arising in IR and shown to be an effective approach for formulating an enhanced semantic model that captures the latent term‐document structure of the data. However, in terms of storage and computation requirements, its implementation can be much improved for large‐scale applications. We discuss an efficient and reliable algorithm, called SPK‐RSVD‐LSI, as an alternative approach for deriving the enhanced semantic model. The algorithm combines the generalized Riemannian SVD and the Lanczos method with full reorthogonalization and explicit restart strategies. We demonstrate that our approach performs as well as the original low‐rank Riemannian SVD method by comparing their retrieval performance on a well‐known benchmark document collection. Copyright 2004 John Wiley & Sons, Ltd.