卡尔费休法测定己烷中微量水分

介绍了以甲醇溶液为溶剂,用卡尔-费休滴定法测定己烷中微量水分,找到最佳实验条件,得到了理想的分析结果.方法快速、准确、重现性好,满足测定需求.     

局部线性判别分析及其在掌纹识别中的应用

在线性判别分析(LDA)算法基础上强调图像类间数据的局部可分性,提出一种称为局部LDA(LLDA)的新子空间方法,并给出LLDA的图嵌入表示。针对LLDA同样存在的小样本问题,首先给出了传统适于LDA的主成分分析(PCA)预处理方法不适于LLDA算法的证明;进而提出了基于散度差判别准则(SDDC)的LLDA(SLLDA),既克服了LLDA的小样本问题,又提供了真实比较LLDA和LDA的平台。在PolyU掌纹数据库上的实验结果表明本文提出的SLLDA算法用于识别的有效性,也验证了数据局部关系对分类的重要性。     

基于HPLC指纹图谱鉴别四个不同品种的金银花叶

利用金银花叶的化学物质群差异,开发金银花不同品种之间的鉴别技术。以灰毡毛忍冬、红腺忍冬、“北花1号”金银花、四季金银花的叶片为实验材料,采用高效液相色谱仪(HPLC)构建金银花叶的化学指纹图谱,从中提取了34个特征色谱峰,结合主成分分析、系统聚类分析等多变量统计分析方法,比较分析了4种不同品种的金银花叶的化学物质群差异。结果显示,前两个主成分累计表征了51.3%的原始变量,在主成分得分图上4个不同品种的金银花样品呈现各自相对独立的空间分布特征。聚类分析可将32个供试样品按品种来源及其相似程度聚为4类。相较于四季金银花,“北花1号”品种与之具有最相似化学特征,其次依次为红腺忍冬和灰毡毛忍冬。指纹图谱中的第5、6、7(绿原酸)、8、11、14、16、18、24、29号等10个色谱峰,是各品种金银花叶化学差异的主要来源,可以进一步开发成为稳定的化学标记。本研究所建立的HPLC指纹图谱及其分析方法,可以用于灰毡毛忍冬、红腺忍冬、“北花1号”金银花、四季金银花等不同品种金银花之间的鉴别分析。     

一种邻域保持判别嵌入人脸识别方法

邻域保持嵌入(NPE)是一种子空间学习方法,具有保持数据流形上局部邻域结构信息的能力。为了进一步提高NPE的性能,本文提出了一种邻域保持判别嵌入(NPDE)算法,并将其用于人脸识别。在NPDE算法中引入了数据集的判别信息,在保持局部邻域结构信息的同时,具有更强的判别力。在Yale和ORL人脸数据库上的实验结果表明,本文提出的NPDE用于人脸识别具有较高的识别率。     

The Advisory Working Alliance Inventory--Advisor Version: Scale Development and Validation.

The development of the Advisory Working Alliance Inventory--Advisor Version (AWAI-A) is presented. In the first study, data from 236 faculty members from APA-accredited counseling psychology programs were subjected to a principal components analysis, yielding 3 subscales: Rapport (15 items), Apprenticeship (8 items), and Task Focus (8 items). The data also revealed evidence of the AWAI-A's internal consistency and concurrent validity, the latter being demonstrated by correlations with measures of satisfaction with the advising relationship, costs and benefits of advising, advisee interest in science and practice, and advisee research self-efficacy. In the 2nd study, data from 44 additional advisors yielded evidence of the AWAI-A's test-retest reliability and discriminant validity. Implications of the advising working alliance for doctoral training are discussed, and suggestions are provided for future research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Over-excavation forecast of underground opening by using Bayes discriminant analysis method

A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA) theory was presented. The Bayes discriminant analysis theory was introduced. Based on an engineering example, the factors influencing the over-excavation of underground opening were taken into account to build a forecast BDA model, and the prior information about over-excavation of underground opening was also taken into consideration. Five parameters influencing the over-excavation of opening, including 2 groups of joints, 1 group of layer surface, extension and space between structure faces were selected as geometric parameters. Engineering data in an underground opening were used as the training samples. The cross-validation method was introduced to verify the stability of BDA model and the ratio of mistake-discrimination was equal to zero after the BDA model was trained. Data in an underground engineering were used to test the discriminant ability of BDA model. The results show that five forecast results are identical with the actual situation and BDA can be used in practical engineering.     

Testing the convergent and discriminant validity of the Decisional Balance Scale of the Transtheoretical Model using the Multi-Trait Multi-Method approach.

The authors extended research on the construct validity of the Decisional Balance Scale for smoking in adolescence by testing its convergent and discriminant validity. Hierarchical confirmatory factor analysis multi-trait multi-method approach (HCFA MTMM) was used with data from 2,334 UK adolescents, both smokers and non-smokers. They completed computerized and paper versions of the questionnaire on 3 occasions over 2 years. The results indicated a 3-factor solution; Social Pros, Coping Pros, and Cons fit the data best. The HCFA MTMM model fit the data well, with correlated methods and correlated trait factors. Subsequent testing confirmed discriminant validity between the factors and convergent validity of both methods of administering the questionnaire. There was, however, clear evidence of a method effect, which may have arisen due to different response formats or may be a function of the method of presentation. Taken with other data, there is strong evidence for construct validity of Decisional Balance for smoking in adolescence, but evidence of predictive validity is required. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Fault diagnosis based on variable-weighted kernel Fisher discriminant analysis

In this paper, a new fault diagnosis approach with variable-weighted kernel Fisher discriminant analysis (VW-KFDA) is proposed. The approach incorporates the variable weighting into KFDA. The variable weighting finds out the weight vector of each fault by maximizing separation between the normal and each fault data. With continuous non-negative values, each element of the weight vector represents the corresponding variable's contribution to a special fault. After all fault data are weighted by the corresponding weight vectors, KFDA is performed on these weighted fault data. These weight vectors offer important supplemental classification information to KFDA and effectively improve its multi-classification performance. The proposed approach is applied to the Tennessee Eastman process (TEP). The results show superior capability for fault diagnosis to KFDA and FDA.     

Uncorrelated linear discriminant analysis (ULDA): A powerful tool for exploration of metabolomics data

The theory together with an algorithm for uncorrelated linear discriminant analysis (ULDA) is introduced and applied to explore metabolomics data. ULDA is a supervised method for feature extraction (FE), discriminant analysis (DA) and biomarker screening based on the Fisher criterion function. While principal component analysis (PCA) searches for directions of maximum variance in the data, ULDA seeks linearly combined variables called uncorrelated discriminant vectors (UDVs). The UDVs maximize the separation among different classes in terms of the Fisher criterion. The performance of ULDA is evaluated and compared with PCA, partial least squares discriminant analysis (PLS-DA) and target projection discriminant analysis (TP-DA) for two datasets, one simulated and one real from a metabolomic study. ULDA showed better discriminatory ability than PCA, PLS-DA and TP-DA. The shortcomings of PCA, PLS-DA and TP-DA are attributed to interference from linear correlations in data. PLS-DA and TP-DA performed successfully for the simulated data, but PLS-DA was slightly inferior to ULDA for the real data. ULDA successfully extracted optimal features for discriminant analysis and revealed potential biomarkers. Furthermore, by means of cross-validation, the classification model obtained by ULDA showed better predictive ability than PCA, PLS-DA and TP-DA. In conclusion, ULDA is a powerful tool for revealing discriminatory information in metabolomics data.     

Determination of the optimal number of features for quadratic discriminant analysis via the normal approximation to the discriminant distribution

Given the joint feature-label distribution, increasing the number of features always results in decreased classification error; however, this is not the case when a classifier is designed via a classification rule from sample data. Typically, for fixed sample size, the error of a designed classifier decreases and then increases as the number of features grows. The problem is especially acute when sample sizes are very small and the potential number of features is very large. To obtain a general understanding of the kinds of feature-set sizes that provide good performance for a particular classification rule, performance must be evaluated based on accurate error estimation, and hence a model-based setting for optimizing the number of features is needed. This paper treats quadratic discriminant analysis (QDA) in the case of unequal covariance matrices. For two normal class-conditional distributions, the QDA classifier is determined according to a discriminant. The standard plug-in rule estimates the discriminant from a feature-label sample to obtain an estimate of the discriminant by replacing the means and covariance matrices by their respective sample means and sample covariance matrices. The unbiasedness of these estimators assures good estimation for large samples, but not for small samples.Our goal is to find an essentially analytic method to produce an error curve as a function of the number of features so that the curve can be minimized to determine an optimal number of features. We use a normal approximation to the distribution of the estimated discriminant. Since the mean and variance of the estimated discriminant will be exact, these provide insight into how the covariance matrices affect the optimal number of features. We derive the mean and variance of the estimated discriminant and compare feature-size optimization using the normal approximation to the estimated discriminant with optimization obtained by simulating the true distribution of the estimated discriminant. Optimization via the normal approximation to the estimated discriminant provides huge computational savings in comparison to optimization via simulation of the true distribution. Feature-size optimization via the normal approximation is very accurate when the covariance matrices differ modestly. The optimal number of features based on the normal approximation will exceed the actual optimal number when there is large disagreement between the covariance matrices; however, this difference is not important because the true misclassification error using the number of features obtained from the normal approximation and the number obtained from the true distribution differ only slightly, even for significantly different covariance matrices.