遥感影像检索中高维特征的快速匹配

提高特征点匹配效率是将高维局部特征运用于遥感影像检索的关键,该文提出一种新的压缩优先过滤(CPF)索引算法。该算法通过量化特征向量构建近似向量空间上的高维索引结构,利用优先队列过滤得到近似近邻候选集,精确计算候选实际特征向量得到最终近邻。在CPF算法基础上提出了基于快速鲁棒性特征(SURF)的遥感影像快速检索算法。实验及分析表明,与经典的最佳桶优先(BBF)算法相比较,CPF降低了磁盘读写(I/O)和浮点运算次数,特征点数目较大时,查询效率和总体查询精度均有显著提高,基于SURF特征的遥感影像快速检索算法能快速返回正确目标与相似目标影像。     

改进的kNN分类算法在工业物联网中的应用

在工业物联网中,k近邻分类(kNN)被广泛应用于缺陷产品识别和异常检测.但kNN自身存在计算复杂度高、不适用于分布式环境等缺点.因此,文中提出了一种安全有效的分布式kNN分类算法,以防止信息泄漏和控制流泄漏,同时支持分布式服务器上的大规模数据分类.首先设计了一个安全有效的向量同态加密方案.在该方案的基础上,提出了DkN...     

Adaptive partitioning by local density‐peaks: An efficient density‐based clustering algorithm for analyzing molecular dynamics trajectories

We present an efficient density‐based adaptive‐resolution clustering method APLoD for analyzing large‐scale molecular dynamics (MD) trajectories. APLoD performs the k‐nearest‐neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high‐density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2–3 orders of magnitude for systems ranging from alanine dipeptide to a 370‐residue Maltose‐binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low‐density regions, while smaller clusters at high‐density regions), which is a clear advantage over other popular clustering algorithms including k‐centers and k‐medoids. We anticipate that APLoD can be widely applied to split ultra‐large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc.     

Fault diagnosis using kNN reconstruction on MRI variables

In industrial processes, investigating the root causes of abnormal events is a crucial task when process faults are detected; isolating the faulty variables provides additional information for investigating the root causes of the faults. The traditional contribution plot is a popular and perspicuous tool to isolate faulty variables. However, this method can only determine one faulty variable (the biggest contributor) when there are several variables out of control at the same time. In the presented work, a novel fault diagnosis method is derived using k‐nearest neighbor (kNN) reconstruction on maximize reduce index (MRI) sensors; it is aimed at identifying all fault variables precisely. This method can identify the faulty variables effectively through reconstructing MRI variables one by one. A numerical example focuses on validating the performance of kNN missing data analysis method firstly, then multi‐sensors fault identification results are also given. Tennessee Eastman process is provided to demonstrate that the proposed approach can identify the responsible variables for the multiple sensors fault. Copyright © 2015 John Wiley & Sons, Ltd.     

Review of validation and reporting of non-targeted fingerprinting approaches for food authentication

Food fingerprinting approaches are expected to become a very potent tool in authentication processes aiming at a comprehensive characterization of complex food matrices. By non-targeted spectrometric or spectroscopic chemical analysis with a subsequent (multivariate) statistical evaluation of acquired data, food matrices can be investigated in terms of their geographical origin, species variety or possible adulterations. Although many successful research projects have already demonstrated the feasibility of non-targeted fingerprinting approaches, their uptake and implementation into routine analysis and food surveillance is still limited. In many proof-of-principle studies, the prediction ability of only one data set was explored, measured within a limited period of time using one instrument within one laboratory. Thorough validation strategies that guarantee reliability of the respective data basis and that allow conclusion on the applicability of the respective approaches for its fit-for-purpose have not yet been proposed. Within this review, critical steps of the fingerprinting workflow were explored to develop a generic scheme for multivariate model validation. As a result, a proposed scheme for “good practice” shall guide users through validation and reporting of non-targeted fingerprinting results. Furthermore, food fingerprinting studies were selected by a systematic search approach and reviewed with regard to (a) transparency of data processing and (b) validity of study results. Subsequently, the studies were inspected for measures of statistical model validation, analytical method validation and quality assurance measures. In this context, issues and recommendations were found that might be considered as an actual starting point for developing validation standards of non-targeted metabolomics approaches for food authentication in the future. Hence, this review intends to contribute to the harmonization and standardization of food fingerprinting, both required as a prior condition for the authentication of food in routine analysis and official control.     

IC参数成品率全局优化的映射距离最小化算法

提出了一种新的集成电路参数成品率的全局优化算法--映射距离最小化算法.该算法采用了均匀设计与映射距离最小的耦合优化,每次迭代模拟次数很少,优化过程明显加速.另外,给出了一种粗略估计空间点集均匀性的方法--k近邻密度估计,在有效时间内判断一个空间点集的均匀性.模拟结果表明,该算法对集成电路进行快速成品率优化设计及提高电路设计的稳定性具有较好的应用价值.     

Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds

A novel method (in the context of quantitative structure–activity relationship (QSAR)) based on the k nearest neighbour (kNN) principle, has recently been introduced for the derivation of predictive structure–activity relationships. Its performance has been tested for estimating the estrogen binding affinity of a diverse set of 142 organic molecules. Highly predictive models have been obtained. Moreover, it has been demonstrated that consensus-type kNN QSAR models, derived from the arithmetic mean of individual QSAR models were statistically robust and provided more accurate predictions than the great majority of the individual QSAR models. Finally, the consensus QSAR method was tested with 3D QSAR and log?P data from a widely used steroid benchmark data set.     

Estimating Relative Risk on the Line Using Nearest Neighbor Statistics

This paper considers a non-parametric method for identifying intervals on the line where the relative risk of cases to controls exceeds a pre-specified level. The method is based on the kth nearest neighbor (kNN) approach for density estimation. An asymptotic result is presented that yields an explicit formula for constructing a confidence interval for the relative risk at a given point. Numerical simulations are used to compare this approach with a kernel density estimation procedure. An application is made to a case-control study in which the relative risk of motor vehicle crashes caused by female drivers is compared to male drivers in the state of Kentucky as a function of age and then by time of day.      

基于LSH的高维大数据k近邻搜索算法

局部敏感哈希(LSH)及其变体是解决高维数据k近邻(kNN)搜索的有效算法.但是,随着数据规模的日趋庞大,传统的集中式LSH算法结构已经不能够满足大数据时代的需求.本文分析传统LSH方案的不足之处,拓展AND-OR结构,提出通过索引而不比较原始数据直接实现高维大数据k近邻搜索算法C2SLSH.理论分析和实验证明,C2SLSH在分布式平台下具有稳定的可扩展性,在保证同等精确率的情况下,处理速度大约是现有方法的3倍.