Taxonomic studies in wall barley (Hordeum murinum sensu lato) and sea barley (Hordeum marinum sensu lato). 2. Multivariate morphometrics

1984 ◽  
Vol 62 (12) ◽  
pp. 2754-2764 ◽  
Author(s):  
Bernard R. Baum ◽  
L. Grant Bailey
Keyword(s):  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Sensu Stricto ◽  
Data Matrix ◽  
Multivariate Morphometrics ◽  
Stepwise Discriminant Analysis ◽  
Linear Discriminant ◽  
Specific Rank ◽  
Considerable Controversy ◽  
Taxonomic Studies

The demarcation of the Old World species in Hordeum L. section Hordeastrum Doell. has been the subject of considerable controversy. The present paper reports the results of a morphometric analysis of the wall and sea barleys (H. murinum L., H. marinum Huds., and their allies). A sample of 227 accessions was scored for 39 characters, and the resulting data matrix was divided into three groups on the basis of lodicules and cpiblast characters. These three groups were then subjected to several aspects of discriminant analysis (stepwise discriminant analysis, linear discriminant analysis, canonical analysis of discriminance, and nearest neighbor discriminant analysis) both on untransformed and log-transformed data. The results indicate that the wall and sea barleys consist of five distinct groups worthy of morphological specific rank. These correspond to H. marinum sensu stricto, H. geniculatum All., H. glaucum Steudel. H. murinum sensu stricto, and H. leporinum Link. Ranges for 37 characters within these five taxa are presented. A key to the five species and one hybrid (reported in the first paper of this series) is supplied. Our conclusions are discussed in the context of various taxonomic treatments of the group.

scholarly journals Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis dan k Nearest Neighbor

2017 ◽  
Vol 9 (1) ◽  
pp. 1-9
Author(s):  
Fandiansyah Fandiansyah ◽  
Jayanti Yusmah Sari ◽  
Ika Putri Ningrum
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbor ◽  
Experimental Result ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Nearest Neighbor Classifier ◽  
Neighbor Classifier

Face recognition is one of the biometric system that mostly used for individual recognition in the absent machine or access control. This is because the face is the most visible part of human anatomy and serves as the first distinguishing factor of a human being. Feature extraction and classification are the key to face recognition, as they are to any pattern classification task. In this paper, we describe a face recognition method based on Linear Discriminant Analysis (LDA) and k-Nearest Neighbor classifier. LDA used for feature extraction, which directly extracts the proper features from image matrices with the objective of maximizing between-class variations and minimizing within-class variations. The features of a testing image will be compared to the features of database image using K-Nearest Neighbor classifier. The experiments in this paper are performed by using using 66 face images of 22 different people. The experimental result shows that the recognition accuracy is up to 98.33%. Index Terms—face recognition, k nearest neighbor, linear discriminant analysis.

scholarly journals Combination of an E-Nose and an E-Tongue for Adulteration Detection of Minced Mutton Mixed with Pork

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaojing Tian ◽  
Jun Wang ◽  
Zhongren Ma ◽  
Mingsheng Li ◽  
Zhenbo Wei
Keyword(s):  
Discriminant Analysis ◽  
Partial Least Square ◽  
Data Matrix ◽  
Classification Error ◽  
Combined System ◽  
Validation Data ◽  
Data Set ◽  
Linear Discriminant ◽  
Calibration And Validation ◽  
Organoleptic Characteristics

An E-panel, comprising an electronic nose (E-nose) and an electronic tongue (E-tongue), was used to distinguish the organoleptic characteristics of minced mutton adulterated with different proportions of pork. Meanwhile, the normalization, stepwise linear discriminant analysis (step-LDA), and principle component analysis were employed to merge the data matrix of E-nose and E-tongue. The discrimination results were evaluated and compared by canonical discriminant analysis (CDA) and Bayesian discriminant analysis (BAD). It was shown that the capability of discrimination of the combined system (classification error 0%∼1.67%) was superior or equable to that obtained with the two instruments separately, and E-tongue system (classification error for E-tongue 0∼2.5%) obtained higher accuracy than E-nose (classification error 0.83%∼10.83% for E-nose). For the combined system, the combination of extracted data of 6 PCs of E-nose and 5 PCs of E-tongue was proved to be the most effective method. In order to predict the pork proportion in adulterated mutton, multiple linear regression (MLR), partial least square analysis (PLS), and backpropagation neural network (BPNN) regression models were used, and the results were compared, aiming at building effective predictive models. Good correlations were found between the signals obtained from E-tongue, E-nose, and fusion data of E-nose and E-tongue and proportions of pork in minced mutton with correlation coefficients higher than 0.90 in the calibration and validation data sets. And BPNN was proved to be the most effective method for the prediction of pork proportions with R2 higher than 0.97 both for the calibration and validation data set. These results indicated that integration of E-nose and E-tongue could be a useful tool for the detection of mutton adulteration.

NEAREST NEIGHBOR DISCRIMINANT ANALYSIS

2006 ◽  
Vol 20 (08) ◽  
pp. 1245-1259 ◽  
Author(s):  
XIPENG QIU ◽  
LIDE WU
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Small Sample Size ◽  
Small Sample ◽  
Real World Data ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Gaussian Density

Linear Discriminant Analysis (LDA) is a popular feature extraction technique in statistical pattern recognition. However, it often suffers from the small sample size problem when dealing with high-dimensional data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper, a novel nonparametric linear feature extraction method, nearest neighbor discriminant analysis (NNDA), is proposed from the view of the nearest neighbor classification. NNDA finds the important discriminant directions without assuming the class densities belong to any particular parametric family. It does not depend on the nonsingularity of the within-class scatter matrix either. Then we give an approximate approach to optimize NNDA and an extension to k-NN. We apply NNDA to the simulated data and real world data, the results demonstrate that NNDA outperforms the existing variant LDA methods.

Comparisons of classification methods for viral genomes and protein families using alignment-free vectorization

2018 ◽  
Vol 17 (4) ◽  
Author(s):  
Hsin-Hsiung Huang ◽  
Shuai Hao ◽  
Saul Alarcon ◽  
Jie Yang
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbor ◽  
Genomic Analysis ◽  
Support Vector ◽  
Comparative Genomic ◽  
Classification Methods ◽  
Statistical Classification ◽  
Linear Discriminant ◽  
Viral Genomes

Abstract In this paper, we propose a statistical classification method based on discriminant analysis using the first and second moments of positions of each nucleotide of the genome sequences as features, and compare its performances with other classification methods as well as natural vector for comparative genomic analysis. We examine the normality of the proposed features. The statistical classification models used including linear discriminant analysis, quadratic discriminant analysis, diagonal linear discriminant analysis, k-nearest-neighbor classifier, logistic regression, support vector machines, and classification trees. All these classifiers are tested on a viral genome dataset and a protein dataset for predicting viral Baltimore labels, viral family labels, and protein family labels.

scholarly journals Vehicle Reidentification in a Connected Vehicle Environment using Machine Learning Algorithms

2018 ◽  
Vol 2672 (45) ◽  
pp. 160-172 ◽  
Author(s):  
Zuoyu Miao ◽  
K. Larry Head ◽  
Byungho Beak
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Linear Discriminant

Deployment of connected vehicles will become possible for most American cities in the next 10 to 20 years. Connected vehicle (CV) applications (e.g., mobility, safety, environment) are constantly receiving vehicle data. The current ID protection mechanism assumes a vehicle’s ID changes every 5 minutes, so the topic of rematching vehicles is of interest in privacy protection and performance measure research. This paper explores the possibility of rematching connected vehicles’ IDs using popular machine learning techniques, including logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), linear and nonlinear support vector machine (SVM) and nearest neighbor algorithms. An experiment is conducted using a microscopic traffic simulation model through a software-in-the-loop technique. The best average mismatching rate is 14%. To assess potential factors’ effects on matching accuracy, a Poisson mixed regression model is analyzed under the Bayesian inference framework. Findings are: different matching algorithms vary in matching performance and the linear SVM, the QDA and the LDA have the best accuracy results; traffic volume and market penetration rate have little impact on matching results; location and number of vehicles to be matched are considered significant. The results make the performance measurement of future CV applications feasible and also suggest that more secure mechanisms are needed to protect the public.

Linear Discriminant Analysis for Prediction of Group Membership: A User-Friendly Primer

2019 ◽  
Vol 2 (3) ◽  
pp. 250-263 ◽  
Author(s):  
Peter Boedeker ◽  
Nathan T. Kearns
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Best Practice ◽  
Nearest Neighbor ◽  
Multinomial Logistic Regression ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Prior Specification

In psychology, researchers are often interested in the predictive classification of individuals. Various models exist for such a purpose, but which model is considered a best practice is conditional on attributes of the data. Under certain conditions, linear discriminant analysis (LDA) has been shown to perform better than other predictive methods, such as logistic regression, multinomial logistic regression, random forests, support-vector machines, and the K-nearest neighbor algorithm. The purpose of this Tutorial is to provide researchers who already have a basic level of statistical training with a general overview of LDA and an example of its implementation and interpretation. Decisions that must be made when conducting an LDA (e.g., prior specification, choice of cross-validation procedures) and methods of evaluating case classification (posterior probability, typicality probability) and overall classification (hit rate, Huberty’s I index) are discussed. LDA for prediction is described from a modern Bayesian perspective, as opposed to its original derivation. A step-by-step example of implementing and interpreting LDA results is provided. All analyses were conducted in R, and the script is provided; the data are available online.

Crime Detection with ICA and Artificial Intelligent Approach

2013 ◽  
Vol 816-817 ◽  
pp. 616-622
Author(s):  
Ahmad Kadri Junoh ◽  
Muhammad Naufal Mansor ◽  
Alezar Mat Ya'acob ◽  
Farah Adibah Adnan ◽  
Syafawati Ab. Saad ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Independent Component Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Violent Crimes ◽  
Artificial Intelligent ◽  
Linear Discriminant ◽  
Crime Detection ◽  
Intelligent Approach

The Rise of Crime in Malaysia reported that violent crimes comprised only 10% of reported crimes each year and the majority of crimes, 90%, were classified as property crimes. However, the ratio of police to population is 3.6 officers to 1,000 citizens in Malaysia. This lack of manpower sources ratios alone are not a comprehensive afford of crime fighting capabilities. Thus, we proposed an Artificial Intelligent Techniques to determine the behaviour of the burglar with Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and k Nearest Neighbor (k-NN) Classifier. This system provided a good justification as a monitoring supplementary tool for the Malaysian police arm forced.

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