主成分分析和聚类分析在花卉栽培基质配方选择中的应用

基质栽培是工厂化无土栽培的一个重要分支。结合牡丹品种胡红牡丹的生产实际,采用多元统计中主成分分析法、聚类分析法,对14种栽培基质配方进行了优劣等级的划分。首先确立胡红牡丹的农艺指标体系,然后用主成分分析法对指标体系进行筛选,接着用聚类分析得出基质配方的优劣等级,最后就各优劣等级进行了概述。     

主成分聚类分析在战储器材轮换中的应用

针对传统轮换方法在战储器材轮换上的缺陷和不足,从器材重要度的角度,提出一种基于主成分聚类分析的战储航材轮换方法。在综合考虑传统方法的不足和航材管理特性的基础上,构建战储器材轮换指标体系。首先根据指标集运用主成分分析法进行降维处理,然后运用SPSS软件的系统聚类法对其聚类分析,最后根据分析结果采取相应的轮换。实例结果表明该方法是合理、有效的。     

基于主成分分析和聚类分析的各地区医疗水平状况研究

为了研究全国各地区医疗发展状况,本文以31个地区为主要研究对象,选取了医疗卫生机构数、三级医院数、卫生人员数、医疗卫生机构床位数、卫生总费用等10个指标,数据均来自2019中国卫生健康统计年鉴,基于主成分分析(PCA)和聚类分析,运用SPSS软件对评价指标进行标准化,降维和去相关,同时对多元数据进行合理的分类,最后对各地区的医疗建设和发展水平进行评价。     

R软件在主成分分析中的应用研究

在研究复杂问题时,主成分分析方法可以抓住问题的主要矛盾,揭示其内部各因素之间的规律性,提高分析的效率。R软件是一款免费且功能强大的软件,研究表明R软件可以方便快捷地完成主成分分析的计算,且具有很高的计算精度。     

软件度量中主成分分析方法的研究

文中针对软件度量的数据分析阶段存在大量相关性数据的情况,提出将主成分分析方法引入数据的分析过程。在尽可能多地保留原始数据信息的基础上,使软件度量所涉及的大量相关数据得到降维,筛选出影响被度量对象的主要因素,在降维原始数据的同时使度量的结果更加准确,度量数据的含义更加清晰明确,以达到使整个度量过程更加准确、高效的目的。     

软件度量中主成分分析方法的研究

文中针对软件度盘的数据分析阶段存在大量相关性数据的情况，提出将主成分分析方法引入数据的分析过程。在尽可能多地保留原始数据信息的基础上，使软件度量所涉及的大量相关数据得到降维，筛选出影响被度量对象的主要因素，在降维原始数据的同时使度量的结果更加准确，度量数据的含义更加清晰明确，以达到使整个度量过程更加准确、高效的目的。     

基于主成分聚类分析的煤矿安全评价模型

首先从人的行为、安全管理、装备设施、自然条件、安全技术与监管机制5个方面建立了煤矿安全评价指标体系;然后运用主成分分析和聚类分析建立了煤矿安全评价模型,通过主成分分析选取综合指标,减少了评价指标的个数,通过聚类分析对各煤矿企业的安全状态进行分类评价,分析其相似性和差异性;最后通过对某省40个煤矿企业的安全状况进行评价,介绍了该煤矿安全评价模型的应用步骤。应用结果表明,该煤矿安全评价模型得出的评价结果简单、直观地反映了煤矿的安全状态。     

主成分分析法在软件静态测试中的研究与应用

随着信息化程度的不断提高以及人们对软件需求的扩大,软件的复杂性也已经远远地超出了以前的水平,大大地增加了软件设计和开发的难度.以软件复杂性为出发点,介绍了主成分分析法(PCA)的基本思想、原理和主要作用,分析了主成分分析法在软件静态测试中的应用价值与可行性,最后通过一个具体的软件进行了详细的算例分析,获得了较好的效果,帮助软件开发人员和测试人员在静态分析中识别复杂性和风险性比较高的函数和模块起到了很好的作用.     

白化主成分分析类算法在人脸识别中的应用

针对能量谱的不平衡性会影响人脸识别效果的问题,基于白化脸的概念提出了白化主成分分析类算法的框架.该算法框架使用1个白化滤波器和1个低通滤波器对原始图像进行预处理,然后结合传统的PCA类算法提取特征向量(或矩阵),最后通过k-NN分类方法进行人脸识别.利用ORL人脸图像库进行实验,实验结果表明该算法框架改善了人脸识别的效...     

基于鲁棒主成分分析的人脸子空间重构方法

子空间方法是人脸识别中的经典方法,其基本假设是人脸图像处于高维图像空间的低维子空间中.但是,由于光照变化、阴影、遮挡、局部镜面反射、图像噪声等因素的影响,使得子空间假设难以满足.为此,提出一种基于鲁棒主成分分析的人脸子空间重构方法.该方法将人脸图像数据矩阵表示为满足子空间假设的低秩矩阵和表征光照变化、阴影、遮挡、局部镜面反射、图像噪声等因素的误差矩阵之和,利用鲁棒主成分分析法求解低秩矩阵和误差矩阵.实验结果表明,文中方法能够有效地重构人脸图像的低维子空间.     

Subpattern-based principle component analysis

We propose a subpattern-based principle component analysis (SpPCA). The traditional PCA operates directly on a whole pattern represented as a vector and acquires a set of projection vectors to extract global features from given training patterns. SpPCA operates instead directly on a set of partitioned subpatterns of the original pattern and acquires a set of projection sub-vectors for each partition to extract corresponding local sub-features and then synthesizes them into global features for subsequent classification. The experimental results show that the proposed SpPCA has (much) better classification performances on all the real-life benchmark datasets than PCA.     

基于主成分分析的字典学习

在??奇异值字典学习方法的基础上,结合主成分分析方法提出了??主成分分析字典学习方法。该方法取代了??奇异值分解(KSVD)方法中对误差项直接进行SVD分解来更新原子,取而代之的是通过对误差项进行PCA分解,提取其主成分作为字典中原子的更新。仿真结果表明,与KSVD字典学习方法相比,所提出的方法字典学习效果更好,对训练样本的表达误差更小,学习字典更能表达训练样本的特征。     

基于BP神经网络的主分量分析人脸识别算法

提出了基于BP神经网络的主分量人脸识别算法。该算法首先用小波变换对人脸图像进行小波分解,形成低频小波子图,然后用主分量分析法构造特征脸子空间,将人脸图像在特征空间的投影作为BP神经网络的输入,由BP神经网络和后验概率转换器构成人脸识别器。针对ORL人脸库的实验结果表明该方法具有较高的识别率。     

Model-based cluster and discriminant analysis with the MIXMOD software

The Mixture Modeling (MIXMOD) program fits mixture models to a given data set for the purposes of density estimation, clustering or discriminant analysis. A large variety of algorithms to estimate the mixture parameters are proposed (EM, Classification EM, Stochastic EM), and it is possible to combine these to yield different strategies for obtaining a sensible maximum for the likelihood (or complete-data likelihood) function. MIXMOD is currently intended to be used for multivariate Gaussian mixtures, and fourteen different Gaussian models can be distinguished according to different assumptions regarding the component variance matrix eigenvalue decomposition. Moreover, different information criteria for choosing a parsimonious model (the number of mixture components, for instance) are included, their suitability depending on the particular perspective (cluster analysis or discriminant analysis). Written in C++, MIXMOD is interfaced with SCILAB and MATLAB. The program, the statistical documentation and the user guide are available on the internet at the following address: http://www-math.univ-fcomte.fr/mixmod/index.php.     

基于PCA的坐姿行为识别*

为了对坐姿下的几种行为进行识别,在分析常有坐姿的基础上,提出了通过PCA对八种不同姿势进行分类识别的方法。结合背景帧信息通过背景轮廓消减法提取运动目标区域,利用肤色在YCbCr空间聚集在一片固定区域且在CbCr平面上投影为一个近似椭圆的特性,在运动目标区域提取肤色区域,并对检测出的肤色灰度图进行PCA运算,实现了姿势识别。实验结果表明,所提出的利用PCA进行姿势识别的方法正确率达到84.92%,能够准确地识别坐姿行为,并且对运动阴影、光线变化具有良好的鲁棒性。     

Verification of evolving software via component substitutability analysis

This paper presents an automated and compositional procedure to solve the substitutability problem in the context of evolving software systems. Our solution contributes two techniques for checking correctness of software upgrades: (1) a technique based on simultaneous use of over-and under-approximations obtained via existential and universal abstractions; (2) a dynamic assume-guarantee reasoning algorithm—previously generated component assumptions are reused and altered on-the-fly to prove or disprove the global safety properties on the updated system. When upgrades are found to be non-substitutable, our solution generates constructive feedback to developers showing how to improve the components. The substitutability approach has been implemented and validated in the ComFoRT reasoning framework, and we report encouraging results on an industrial benchmark. This is an extended version of a paper, Dynamic Component Substitutability Analysis, published in the Proceedings of the Formal Methods 2005 Conference, Lecture Notes in Computer Science, vol. 3582, by the same authors. This research was sponsored by the National Science Foundation under grant nos. CNS-0411152, CCF-0429120, CCR-0121547, and CCR-0098072, the Semiconductor Research Corporation under grant no. TJ-1366, the US Army Research Office under grant no. DAAD19-01-1-0485, the Office of Naval Research under grant no. N00014-01-1-0796, the ICAST project and the Predictable Assembly from Certifiable Components (PACC) initiative at the Software Engineering Institute, Carnegie Mellon University. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the US government or any other entity.     

Extensions of principle component analysis with applications on vision based computing

This paper mainly focuses on the principle component analysis (PCA) and its applications on vision based computing. The underlying mechanism of PCA given and several significant factors, involved with subspace training are discussed theoretically in detail including principle components energy, residuals assessment, and decomposition computation. The typical extensions, including probabilistic PCA (PPCA), kernel PCA (KPCA), multi-dimensional PCA and robust PCA (RPCA), have been presented with critical analysis on both mechanisms and computations. Combining with the studies on, such as, image compression, visual tracking, image recognition and super-resolution image reconstruction, PCA and its extensions applied to computer vision are critically reviewed and evaluated on the targeted issues in each application as well as the role they played at specific tasks to the characteristics, cost and suitable situations of each PCA based vision processing technique.     

Understanding software project risk: a cluster analysis

Understanding software project risk can help in reducing the incidence of failure. Building on prior work, software project risk was conceptualized along six dimensions. A questionnaire was built and 507 software project managers were surveyed. A cluster analysis was then performed to identify aspects of low, medium, and high risk projects. An examination of risk dimensions across the levels revealed that even low risk projects have a high level of complexity risk. For high risk projects, the risks associated with requirements, planning and control, and the organization become more obvious. The influence of project scope, sourcing practices, and strategic orientation on project risk dimensions was also examined. Results suggested that project scope affects all dimensions of risk, whereas sourcing practices and strategic orientation had a more limited impact. A conceptual model of project risk and performance was presented.     

基于双向二维加权主元分析的人脸表情识别

提出了一种双向二维加权主元分析方法用于人脸表情特征提取,该方法从水平和垂直两个方向对图像矩阵进行降维处理,大幅降低了所提取的特征数目;且考虑到人脸不同部位包含不同的表情信息这一特点,对各个特征赋予不同的权重系数。实验证明,与已有的二维主元分析相比较,该方法不但运算速度快,且获得了更高的识别率。     

MCAI软件主成分分析法评价模型

利用系统论的方法,运用教育学、心理学原理,根据软件质量标准,对影响MCAI软件质量的因素进行分析,建立了MCAI软件评价指标体系。利用主成分分析法对MCAI软件评价体系进行数据变换,用较少的指标来代替原来的指标,由于新指标是原来的指标的线性组合,所以能充分反映原来指标的信息,使得对MCAI软件的评价模型集中在主导因素,而且大大简化MCAI软件评价指标体系,实现了对MCAI软件准确估量和评价。