EOF、SVD和POD的数学统一

经验正交函数(EOF)、奇异值分解(SVD)和适当正交分解(POD)是三种常见的通过获取高维数据的低维近似来进行数据分析的方法.虽然在实际应用中,结合不同的研究问题和研究目的,会采用不同的方法,但是在数学原理上,三种方法都可归结为通过寻求已有数据集合的基向量来实现对原始数据的线性表示.本文以EOF为出发点,通过分析展开系数得出SVD,最后在最优近似表示的原则之下导出POD,揭示三种方法在数学原理上的统一性.     

广义极分解

本文使用下列符号:C~(m×n)表示m×n复矩阵的集合,C_r~(m×n)表示秩为r的m×n复矩阵的集合,A~H和A~+分别表示矩阵A的共轭转置和Moore-Penrose广义逆,|| ||_2表示向量的Euclid范数和矩阵的谱范数,|| ||_F表示Frobenius范数,R(A)表示A的列     

行(列)反对称矩阵的满秩分解和广义逆

本文研究了行(列)转置矩阵与行(列)反对称矩阵的性质.利用分块矩阵理论获得了许多新的结果,给出了行(列)反对称矩阵的满秩分解、秩分解和广义逆的公式及快速算法.它们可极大地减少行(列)反对称矩阵的满秩分解、秩分解和广义逆的计算量与存储量,并且不会丧失数值精度.     

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.     

解非线性方程组的两种区间迭代法

本文为改善(Ⅱ)的计算量、计算稳定性、收敛区域和收敛速度对(Ⅱ)作了修改,提出了两种修改算法。 文章中小写字母表示实空间中的数、向量、矩阵和函数,大写字母表示区间、区间向量、区间矩阵和区间函数。如果区间矩阵的左、右端点矩阵为、,则记=[a,     

广义行(列)酉对称矩阵的Moore-penrose逆

通过对母矩阵进行奇异值分解的方法得到广义行(列)酉对称矩阵的奇异值分解进一步得到其Moore-penrose逆;用谱分解方法得到母矩阵的Moore-penrose逆,进一步得到广义行(列)酉对称矩阵的Moore-penrose逆.     

关于酉对称矩阵的QR分解及其算法

为了简化大型行(列)酉对称矩阵的QR分解,研究了行(列)酉对称矩阵的性质,获得了一些新的结果,给出了行(列)酉对称矩阵的QR分解的公式和快速算法,它们可极大地减少行(列)酉对称矩阵的QR分解的计算量与存储量,并且不会丧失数值精度.同时推广和丰富了邹红星等(2002)的研究内容,拓宽了实际应用领域的范围.     

用向量的线性分解理解函数的展开

用向量的线性表示理解函数的分解,提供向量正交与函数正交关系的一种理解,从而可求得函数分解的系数,再扩展到傅里叶级数和泰勒展开式.     

多维周期双正交向量小波的构造

本文研究了多维周期双正交向量小波的构造.通过使用矩阵分解,给出具有矩阵伸缩的周期双正交向量小波构造的一种算法.     

Possibilistic networks for information retrieval

This paper proposes an information retrieval (IR) model based on possibilistic directed networks. The relevance of a document w.r.t a query is interpreted by two degrees: the necessity and the possibility. The necessity degree evaluates the extent to which a given document is relevant to a query, whereas the possibility degree evaluates the reasons of eliminating irrelevant documents. This new interpretation of relevance led us to revisit the term weighting scheme by explicitly distinguishing between informative and non-informative terms in a document. Experiments carried out on three standard TREC collections show the effectiveness of the model.     

Mining web documents to find additional query terms using fuzzy association rules

In this paper, we present an application of association rules to query refinement. Starting from an initial set of documents retrieved from the web, text transactions are constructed and association rules are extracted. A fuzzy extension of text transactions and association rules is employed, where the presence of the terms (items) in the documents (transactions) is determined with a value between 0 and 1. The obtained rules offer the user additional terms to be added to the query with the purpose of guiding the search and improving the retrieval.     

基于模式约束的XML数据存储和索引技术

对XML数据建立有效的索引,是左右XML数据处理性能的重要因素.现有的索引和存储策略,大部分以丢失结构信息为代价,不利于结构查询和更新.XMLSchema作为描述XML文档结构信息的标准,对文档和查询路径进行有效性验证提供保证,基于此提出了一种基于XMLmSchema模式约束的XML文档数据索引技术SBXI,用于文档数据存储和查询的导航,提高了存储和查询效率,具有较高的空间利用率和较低的索引维护代价,并支持含有多个谓词的复杂查询.     

The Mathematics of Internet Search Engines

This article presents a survey of techniques for ranking results in search engines, with emphasis on link-based ranking methods and the PageRank algorithm. The problem of selecting, in relation to a user search query, the most relevant documents from an unstructured source such as the WWW is discussed in detail. The need for extending classical information retrieval techniques such as boolean searching and vector space models with link-based ranking methods is demonstrated. The PageRank algorithm is introduced, and its numerical and spectral properties are discussed. The article concludes with an alternative means of computing PageRank, along with some example applications of this new method.     

Lower Dimensional Representation of Text Data Based on Centroids and Least Squares

Dimension reduction in today's vector space based information retrieval system is essential for improving computational efficiency in handling massive amounts of data. A mathematical framework for lower dimensional representation of text data in vector space based information retrieval is proposed using minimization and a matrix rank reduction formula. We illustrate how the commonly used Latent Semantic Indexing based on the Singular Value Decomposition (LSI/SVD) can be derived as a method for dimension reduction from our mathematical framework. Then two new methods for dimension reduction based on the centroids of data clusters are proposed and shown to be more efficient and effective than LSI/SVD when we have a priori information on the cluster structure of the data. Several advantages of the new methods in terms of computational efficiency and data representation in the reduced space, as well as their mathematical properties are discussed.Experimental results are presented to illustrate the effectiveness of our methods on certain classification problems in a reduced dimensional space. The results indicate that for a successful lower dimensional representation of the data, it is important to incorporate a priori knowledge in the dimension reduction algorithms.     

Partial-match retrieval for dynamic files

This paper studies file designs for answering partial-match queries for dynamic files. A partial-match query is a specification of the value of zero or more fields in a record. An answer to a query consists of a listing of all records in the file satisfying the values specified.The main contribution is a general method whereby certain primary key hasing schemes can be extended to partial-match retrieval schemes. These partial-match retrieval designs can handle arbitrarily dynamic files and can be optimized with respect to the number of page faults required to answer a query.We illustrate the method by considering in detail the extension of two recent dynamic primary key hashing schemes.     

Fuzzy measures for color image retrieval

In this paper we present a scheme for fuzzy similarity based strategy to retrieve an image from a library of color images. Color features are among the most important features used in image database retrieval. Due to its compact representation and low complexity, direct histogram comparison is the most commonly used technique in measuring color similarity of images. A gamma membership function, derived from the Gamma distribution, has been proposed to find the membership values of the gray levels of the histogram. We present here an image retrieval scheme with some popular vector fuzzy distance measures using a gamma membership function for finding the membership values of the gray levels and evaluate the matching function to select the appropriate retrieval mechanism.     

Matrix computations for information retrieval and major and outlier cluster detection

In this paper we introduce COV, a novel information retrieval (IR) algorithm for massive databases based on vector space modeling and spectral analysis of the covariance matrix, for the document vectors, to reduce the scale of the problem. Since the dimension of the covariance matrix depends on the attribute space and is independent of the number of documents, COV can be applied to databases that are too massive for methods based on the singular value decomposition of the document-attribute matrix, such as latent semantic indexing (LSI). In addition to improved scalability, theoretical considerations indicate that results from our algorithm tend to be more accurate than those from LSI, particularly in detecting subtle differences in document vectors. We demonstrate the power and accuracy of COV through an important topic in data mining, known as outlier cluster detection. We propose two new algorithms for detecting major and outlier clusters in databases—the first is based on LSI, and the second on COV. Our implementation studies indicate that our cluster detection algorithms outperform the basic LSI and COV algorithm in detecting outlier clusters.     

Properties of topologies of information retrieval systems

This paper studies topological properties of different topologies that are possible on the space of documents as they are induced by queries in a query space together with a similarity function between queries and documents. The main topologies studied here are the retrieval topology (introduced by Everett and Cater) and the similarity topology (introduced by Egghe and Rousseau).The studied properties are the separation properties T₀, T₁, and T₂ (Hausdorff), proximity and connectedness. Full characterizations are given for the diverse topologies to be T₀, T₁, or T₂. It is shown that the retrieval topology is not necessarily a proximity space, while the similarity topology and the pseudo-metric topology always are proximity spaces. A characterization of connectedness in terms of the Boolean NOT-operator is given, hereby showing the intimate relationship between IR and topology.