基于层次搜索的潜在语义索引方法研究

针对潜在语义索引(Latent Semantic Indexing，LSI)计算成本很大，不利于扩展信息检索领域的特点，提出引进图论中的层次搜索方法(Level Search Scheme，LS)，设计出一种针对多主题数据集、融合信息检索和信息过滤的新型算法。该方法能够在信息过滤阶段保持高的查全率，在信息检索阶段保证高的查准率，从而最后达到很好的检索效果。 〔关键词〕 潜在语义索引　层次搜索　奇异值分解(SVD)     

Advanced Linear Algebra: A Call for the Early Introduction of Complex Numbers

Abstract

A second course in linear algebra that goes beyond the traditional lower-level curriculum is increasingly important for students of the mathematical sciences. Although many applications involve only real numbers, a solid understanding of complex arithmetic often sheds significant light. Many instructors are unaware of the opportunities afforded by the early introduction of complex arithmetic. Most elementary properties of complex numbers have immediate matrix analogues and many important theorems can be deduced, or at least postulated, from the basics of complex arithmetic alone.     

新的基于奇异值分解和小波变换的图像水印技术

基于传统的奇异值分解技术和小波变换技术,这里将两种技术相结合,提出了一种新的数字图像水印技术。将灰度水印图像嵌入到了载体小波变换分解后的中频子带。仿真实验结果表明,该算法比较有效,能够抵抗多种攻击,具有较好的鲁棒性和透明性。     

Hand-eye calibration with a new linear decomposition algorithm

To solve the homogeneous transformation equation ofthe form AX=XB in hand-eye calibration, where Xrepresents an unknown transformation from the camera to the robot hand, and A and B denote the known movement transformations associated with the robot hand and the camera, respectively, this paper introduces a new linear decomposition algorithm which consists of singular value decomposition followed by the estimation of the optimal rotation matrix and the least squares equation to solve the rotation matrix of X. Without the requirements of traditional methods that A and B be rigid transformations with the same rotation angle, it enables the extension to non-rigid transformations forA and B. The details of our method are given, together with a short discussion of experimental results, showing that more precision and robustness can be achieved.     

基于矩阵奇异值分解的小波包域灰度级数字水印技术

由于灰度水印具有很好的可视性，引起了越来越多水印研究者的重视。然而灰度水印的数据量太大，如何有效的减少灰度水印嵌入的数据量是一个关键的问题。本文提出了一种基于矩阵奇异值分解的方法，显著的减少了灰度水印嵌入的数据量，同时结合人类视觉系统模型，将水印嵌入到小波包域。试验结果表明，该算法有效的克服了灰度水印数据量大的缺点，并且提高了水印的稳健性。     

F范数及矩阵分解实例研究

本文分别介绍了两种矩阵分解的方法--QR分解和SVD分解.并引入罗贝尼鸟斯(Frdenius)范数对以上两种矩阵分解方法分剐进行降秩度量.最后用实例模拟了SVD分解和F范数评估,得出一些有益的结论.     

基于SVD的图像水印算法缺陷分析及对策

介绍了矩阵奇异值分解的基本原理,分析了现有基于SVD的图像水印算法的缺陷。这种缺陷导致了提取出的水印结果并不是先前嵌入的水印,这样就无法应用于版权保护。结合最新的SVD研究成果,提出了一种相对可靠的解决办法。     

Homomorphic image watermarking with a singular value decomposition algorithm

In this paper, a new homomorphic image watermarking method implementing the Singular Value Decomposition (SVD) algorithm is presented. The idea of the proposed method is based on embedding the watermark with the SVD algorithm in the reflectance component after applying the homomorphic transform. The reflectance component contains most of the image features but with low energy, and hence watermarks embedded in this component will be invisible. A block-by-block implementation of the proposed method is also introduced. The watermark embedding on a block-by-block basis makes the watermark more robust to attacks. A comparison study between the proposed method and the traditional SVD watermarking method is presented in the presence of attacks. The proposed method is more robust to various attacks. The embedding of chaotic encrypted watermarks is also investigated in this paper to increase the level of security.     

融合奇异值分解和动态转移链的学术资源推荐模型

学术资源推荐领域学习者兴趣和学术趋势随时间的变化影响学术资源推荐系统的准确性. 现有大部分推荐方法都没有考虑时间因素. 本文用动态转移链（DTC）对用户兴趣和学术趋势的时效性进行建模. 在DTC框架的基础上,提出一种新的融合矩阵奇异值分解模型（SVD）和动态转移链的学术资源推荐算法（SVD&DTC）. 在数据集SeekSearch上对该方法进行实验,结果表明该算法较之当前流行的主要算法准确率提升3.89%.     

TSP and cluster-based solutions to the reassignment of document identifiers

Recent studies demonstrated that it is possible to reduce Inverted Files (IF) sizes by reassigning the document identifiers of the original collection, as this lowers the distance between the positions of documents related to a single term. Variable-bit encoding schemes can exploit the average gap reduction and decrease the total amount of bits per document pointer. This paper presents an efficient solution to the reassignment problem, which consists in reducing the input data dimensionality using a SVD transformation, as well as considering it a Travelling Salesman Problem (TSP). We also present some efficient solutions based on clustering. Finally, we combine both the TSP and the clustering strategies for reordering the document identifiers. We present experimental tests and performance results in two text TREC collections, obtaining good compression ratios with low running times, and advance the possibility of obtaining scalable solutions for web collections based on the techniques presented here.