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discriminant analysis of terrestrial animal fat and oil adulteration in fish oil by infrared spectroscopy

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International Journal of Agricultural and Biological Engineering 2016年第3期9卷 179-185页

作者： Xu Lingzhi Gao Fei Yang Zengling Han Lujia Liu Xian College of Engineering China Agricultural UniversityBeijing 100083China

In order to improve the effective utilization of animal fats and oils,and then ensure the feeding quality and safety in the field of animal husbandry engineering,the discriminant analysis of different species of terrestrial animal fats and oils in fish oil based on Fourier transform infrared spectroscopy was explored in this ***-seven different species of animal fat and oil materials including fish oil,lard,chicken oil,tallow and suet were *** experimental calibration and validation samples were prepared by adulterating different proportions of terrestrial fat and oil in fish *** show that,it is easy to discriminate different species of raw material of fish oil,lard,chicken oil and ruminant fats based on the infrared spectral characteristics,while the distinction of tallow from suet samples is *** the adulterated samples with percentage range of 1%-60%(w/w),ideal results were obtained to discriminate the terrestrial fat and oil ingredients(lard,chicken oil,tallow and suet)in fish oil,the correct discriminant rates for the independent validation set were all higher than 95%.It was proved by further study that the detection limits for the discriminant analysis of lard,chicken oil,tallow and suet in fish oil were 0.8%,0.6%,2%and 3%,respectively.

关键词： infrared spectroscopy fish oil animal fat and oil discriminant analysis detection limit

discriminant analysis on Land Grading

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Geo-Spatial Information Science 2004年第2期7卷 104-107页

作者： LIUYaolin HOUYajuan professor SchoolofResourceandEnvironmentScienceWuhanUniversity129LuoyuRoadWuhan430079China. 不详

This paper proposes the discriminant analysis on land grading after analyzing the common methods and discussing the Fisher’s discriminant in detail. Actually this method deduces the dimension from multi to single, thus it makes the feature vectors in n \|dimension change to a scalar, and use this scalar to classify samples. This paper illustrates the result by giving an example of the residential land grading by the discriminant analysis.

关键词： land grading discriminant analysis fisher's discriminant

Clustering Seismic Activities Using Linear and Nonlinear discriminant analysis

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Journal of Earth Science 2014年第1期25卷 140-145页

作者： H Serdar Kuyuk Eray Yildirim Emrah Dogan Gunduz Horasan Department of Civil Engineering Faculty of EngineeringSakarya University Department of Geophysical Engineering Faculty of EngineeringSakarya University

Identification and classification of different seismo-tectonic events with similar character- istics in a region of interest is one of the most important subjects in seismic hazard studies. In this study, linear and nonlinear discriminant analyses have been applied to classify seismic events in the vicinity of Istanbul. The vertical components of the digital velocity seismograms are used for seismic events with magnitude （Md） between 1.8 and 3.0 that occurred between 2001 and 2004. Two, time dependent pa- rameters, complexity and S/P peak amplitude ratio are selected as predictands. Linear, quadratic, diag- linear and diagquadratic discriminant functions are investigated. Accuracy of methods with an addi- tional adjusted quadratic models are 96.6%, 96.6%, 95.5%, 96.6%, and 97.6%, respectively with a vari- ous misclassified rate for each class. The performances of models are justified with cross validation and resubstitution error. Although all models remarkably well performed, adjusted quadratic function achieved the best success rate with just 4 misclassified events out of 179, even better compared to com- plex methods such as, self organizing method, k-means, Gaussion mixture models that applied to same dataset in literature.

关键词： discriminant analysis clustering analysis self organizing map k-means Gaussion mix- ture models.

Pose Robust Low-resolution Face Recognition via Coupled Kernel-based Enhanced discriminant analysis

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IEEE/CAA Journal of Automatica Sinica 2016年第2期3卷 203-212页

作者： Xiaoying Wang Haifeng Hu Jianquan Gu the School of Information Science and Technology Sun Yat-sen University

Most face recognition techniques have been successful in dealing with high-resolution (HR) frontal face images. However, real-world face recognition systems are often confronted with the low-resolution (LR) face images with pose and illumination variations. This is a very challenging issue, especially under the constraint of using only a single gallery image per person. To address the problem, we propose a novel approach called coupled kernel-based enhanced discriminant analysis (CKEDA). CKEDA aims to simultaneously project the features from LR non-frontal probe images and HR frontal gallery ones into a common space where discrimination property is maximized. There are four advantages of the proposed approach: 1) by using the appropriate kernel function, the data becomes linearly separable, which is beneficial for recognition; 2) inspired by linear discriminant analysis (LDA), we integrate multiple discriminant factors into our objective function to enhance the discrimination property; 3) we use the gallery extended trick to improve the recognition performance for a single gallery image per person problem; 4) our approach can address the problem of matching LR non-frontal probe images with HR frontal gallery images, which is difficult for most existing face recognition techniques. Experimental evaluation on the multi-PIE dataset signifies highly competitive performance of our algorithm. © 2014 Chinese Association of Automation.

关键词： discriminant analysis Image matching Probes

Sparse discriminant analysis for breast cancer biomarker identification and classification

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Progress in Natural Science:Materials International 2009年第11期19卷 1635-1641页

作者： Yu Shi~(a,b),Daoqing Dai~(a,*),Chaochun Liu~(a,b),Hong Yan~(b,c) a Center for Computer Vision and Department of Mathematics,Sun Yat-Sen(Zhongshan) University,Guangzhou 510275,China b Department of Electric Engineering,City University of Hong Kong,83 Tat Chee Avenue,Kowloon,Hong Kong,China c School of Electrical and Information Engineering,University of Sydney,NSW 2006,Australia Center for Computer Vision and Department of Mathematics Sun Yat-Sen (Zhongshan) University Guangzhou 510275 China Department of Electric Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong China School of Electrical and Information Engineering University of Sydney NSW 2006 Australia

Biomarker identification and cancer classification are two important procedures in microarray data *** propose a novel unified method to carry out both *** first preselect biomarker candidates by eliminating unrelated genes through the BSS/WSS ratio filter to reduce computational cost,and then use a sparse discriminant analysis method for simultaneous biomarker identification and cancer ***,we give a mathematical justification about automatic biomarker *** results show that the proposed method can identify key genes that have been verified in biochemical or biomedical research and classify the breast cancer type correctly.

关键词： Biomarker identification Cancer classification discriminant analysis Maximum penalized likelihood Microarray data analysis

LOCAL CORRELATION discriminant analysis AND ITS SEMI-SUPERVISED EXTENSION

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Journal of Electronics(China) 2011年第3期28卷 289-296页

作者： Chen Caikou Shi Jun （Information Engineering College Yangzhou University Yangzhou 225000 China）

Considering limitations of Linear discriminant analysis (LDA) and Marginal Fisher analysis (MFA), a novel discriminant analysis called Local Correlation discriminant analysis (LCDA) is proposed in this paper. The main idea behind LCDA is to use more robust similarity measure, correlation metric, to measure the local similarity between image data. This results in better classifi-cation performance. In addition, to further improve the discriminant power of LCDA, we extend LCDA to semi-supervised case, which can make use of both labeled and unlabeled data to perform dis-criminant analysis. Extensive experimental results on ORL and AR face databases demonstrate that the proposed LCDA and its semi-supervised version are superior to Principal Component analysis (PCA), LDA, CEA, and MFA.

关键词： Semi-supervised learning Correlation metric discriminant analysis Face recognition

Infant and Under-five Mortality in Bangladesh: discriminant analysis

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Chinese Journal of Population,Resources and Environment 2010年第4期8卷 79-84页

作者： Rahman Md. Mahfuzar IsIam Md. Rafiqul Population Science and Human Resource Development University of Rajshahi

Bangladesh is on target for achieving the Millennium Development Goal 4 relating to infant and under-five mortality because of very rapid reduction in mortality in recent years. But this rate of reduction may be difficult to sustain and may hamper the achievement of Millennium Development Goal 4. Therefore, the main objective of this paper is to discuss and compare the dif- ferent covariates of infant and under-five mortality in the context of overall country, urban and rural levels of Bangladesh using discriminant analysis. For this, the data are taken from Bangladesh Demographic and Health Survey, 2004. In discriminant analysis, the stepwise procedure has been picked up and only the significant variables are ranked according to the rank of Wilk's Lambda val- ues. The canonical discriminant function coefficients (unstandard- ized and standardized) for the predictor variables have also been calculated. Both the results show that breastfeeding is the most important variable in discriminating the two groups of mothers, i.e., mothers experiencing to infant mortality or not and mothers experiencing to under-five mortality or not. The related results of discriminant function also indicate that the discriminant func- tion is statistically significant and discriminates well. Therefore, improvements in the health system are essential for promoting the breastfeeding practices (both inclusive and exclusive), which may be the effective strategies to reach families and communities with targeted messages and information.

关键词： infant and under-five mortality breastfeeding prac- tices discriminant analysis

OSS Project Assessment Based on discriminant analysis and Jump Diffusion Process Model for Fault Big Data

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American Journal of Operations Research 2020年第6期10卷 269-283页

作者： Yoshinobu Tamura Hayato Watanabe Shigeru Yamada Tokyo City University Tokyo Japan Tottori University Tottori Japan

The bug tracking system is well known as the project support tool of open source software. There are many categorical data sets recorded on the bug tracking system. In the past, many reliability assessment methods have been proposed in the research area of software reliability. Also, there are several software project analyses based on the software effort data such as the earned value management. In particular, the software reliability growth models can apply to the system testing phase of software development. On the other hand, the software effort analysis can apply to all development phase, because the fault data is only recorded on the testing phase. We focus on the big fault data and effort data of open source software. Then, it is difficult to assess by using the typical statistical assessment method, because the data recorded on the bug tracking system is large scale. Also, we discuss the jump diffusion process model based on the estimation method of jump parameters by using the discriminant analysis. Moreover, we analyze actual big fault data to show numerical examples of software effort assessment considering many categorical data set.

关键词： Open Source Software Big Fault Data discriminant analysis Open Source Project

Robust Classification through a Nonparametric Kernel discriminant analysis

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Open Journal of Statistics 2022年第4期12卷 443-455页

作者： Macdonald G. Obudho George O. Orwa Romanus O. Otieno Festus A. Were Department of Statistics and Actuarial Sciences Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

The problem of classification in situations where the assumption of normality in the data is violated, and there are non-linear clustered structures in the dataset is addressed. A robust nonparametric kernel discriminant classification function, which is able to address this challenge, has been developed and the misclassification rates computed for various bandwidth matrices. A comparison with existing parametric classification functions such as the linear discriminant and quadratic discriminant is conducted to evaluate the performance of this classification function using simulated datasets. The results presented in this paper show good performance in terms of misclassification rates for the kernel discriminant classifier when the correct bandwidth is selected as compared to other identified existing classifiers. In this regard, the study recommends the use of the proposed kernel discriminant classification rule when one wishes to classify units into one of several categories or population groups where parametric classifiers might not be applicable.

关键词： discriminant analysis Kernel discriminant Nonparametric