scholarly journals Using the Method of Principal Components and Discriminant Analysis with Aim of Simplifying the Selection of Analogue Object for New Area

2021 ◽  
Vol 133 (2) ◽  
pp. 42-46
Author(s):  
R. I. Bulatov ◽  
Keyword(s):  
Discriminant Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Principal Component ◽  
Physical Parameters ◽  
Principal Component Method ◽  
Component Method ◽  
Method Of Principal Components ◽  
New Development ◽  
Selection Of

The article discusses the main geo-physical parameters from 66 development objects. This data is analyzed using component and discriminant analysis methods. Using the principal component method, relatively homogeneous analog groups are distinguished, and discriminant analysis is used to derive the discriminant function and its constants. Based on the values of this function and constants, the new development object is assigned to the analog group.

scholarly journals Polarization selection of radar target using the principal component method

2019 ◽  
Vol 30 ◽  
pp. 12003
Author(s):  
A.B. Borzov ◽  
L.V. Labunetc ◽  
K.P. Likhoedenko ◽  
I.V. Muratov ◽  
G.L. Pavlov ◽  
...  
Keyword(s):  
Target Detection ◽  
Principal Components ◽  
Field Experiments ◽  
Principal Component ◽  
Radar Target ◽  
Principal Component Method ◽  
Component Method ◽  
Detection Algorithms ◽  
Method Of Principal Components ◽  
Selection Of

The results of polarization selection of radar targets using the method of principal components according to the results of modeling and field experiments are presented. Target Detection Algorithms Received

scholarly journals Morphological evaluation of common bean diversity in Bosnia and Herzegovina using the discriminant analysis of principal components (DAPC) multivariate method

Genetika ◽  
2013 ◽  
Vol 45 (3) ◽  
pp. 963-977 ◽  
Author(s):  
Jasmin Grahic ◽  
Fuad Gasi ◽  
Mirsad Kurtovic ◽  
Lutvija Karic ◽  
Mirha Djikic ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Common Bean ◽  
Bosnia And Herzegovina ◽  
Principal Components ◽  
Morphological Characteristics ◽  
Principal Component ◽  
Discriminant Functions ◽  
Factor Loadings ◽  
Geographical Regions ◽  
High Level

In order to analyze morphological characteristics of locally cultivated common bean landraces from Bosnia and Herzegovina (B&H), thirteen quantitative and qualitative traits of 40 P. vulgaris accessions, collected from four geographical regions (Northwest B&H, Northeast B&H, Central B&H and Sarajevo) and maintained at the Gene bank of the Faculty of Agriculture and Food Sciences in Sarajevo, were examined. Principal component analysis (PCA) showed that the proportion of variance retained in the first two principal components was 54.35%. The first principal component had high contributing factor loadings from seed width, seed height and seed weight, whilst the second principal component had high contributing factor loadings from the analyzed traits seed per pod and pod length. PCA plot, based on the first two principal components, displayed a high level of variability among the analyzed material. The discriminant analysis of principal components (DAPC) created 3 discriminant functions (DF), whereby the first two discriminant functions accounted for 90.4% of the variance retained. Based on the retained DFs, DAPC provided group membership probabilities which showed that 70% of the accessions examined were correctly classified between the geographically defined groups. Based on the taxonomic distance, 40 common bean accessions analyzed in this study formed two major clusters, whereas two accessions Acc304 and Acc307 didn?t group in any of those. Acc360 and Acc362, as well as Acc324 and Acc371 displayed a high level of similarity and are probably the same landrace. The present diversity of Bosnia and Herzegovina?s common been landraces could be useful in future breeding programs.

Improved Tropical-Cyclone Flight-Level Wind Estimates Using Routine Infrared Satellite Reconnaissance

2015 ◽  
Vol 54 (2) ◽  
pp. 463-478 ◽  
Author(s):  
John A. Knaff ◽  
Scott P. Longmore ◽  
Robert T. DeMaria ◽  
Debra A. Molenar
Keyword(s):  
Tropical Cyclone ◽  
Principal Components ◽  
Wind Field ◽  
Motion Vector ◽  
Principal Component ◽  
Principal Component Method ◽  
Tangential Wind ◽  
Flight Level ◽  
Polar Grid ◽  
Single Field

AbstractA new and improved method for estimating tropical-cyclone (TC) flight-level winds using globally and routinely available TC information and infrared (IR) satellite imagery is presented. The developmental dataset is composed of aircraft reconnaissance (1995–2012) that has been analyzed to a 1 km × 10° polar grid that extends outward 165 km from the TC center. The additional use of an azimuthally average tangential wind at 500 km, based on global model analyses, allows the estimation of winds at larger radii. Analyses are rotated to a direction-relative framework, normalized by dividing the wind field by the observed maximum, and then decomposed into azimuthal wavenumbers in terms of amplitudes and phases. Using a single-field principal component method, the amplitudes and phases of the wind field are then statistically related to principal components of motion-relative IR images and factors related to the climatological radius of maximum winds. The IR principal components allow the wind field to be related to the radial and azimuthal variability of the wind field. Results show that this method, when provided with the storm location, the estimated TC intensity, the TC motion vector, and a single IR image, is able to estimate the azimuthal wavenumber 0 and 1 components of the wind field. The resulting wind field reconstruction significantly improves on the method currently used for satellite-based operational TC wind field estimates. This application has several potential uses that are discussed within.

scholarly journals A method of dimensionality reduction by selection of components in principal component analysis for text classification

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1499-1506 ◽  
Author(s):  
Yangwu Zhang ◽  
Guohe Li ◽  
Heng Zong
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Principal Components Analysis ◽  
Text Classification ◽  
Principal Components ◽  
Principal Component ◽  
Method Of Dimensionality Reduction ◽  
Components Analysis ◽  
Selection Of

Dimensionality reduction, including feature extraction and selection, is one of the key points for text classification. In this paper, we propose a mixed method of dimensionality reduction constructed by principal components analysis and the selection of components. Principal components analysis is a method of feature extraction. Not all of the components in principal component analysis contribute to classification, because PCA objective is not a form of discriminant analysis (see, e.g. Jolliffe, 2002). In this context, we present a function of components selection, which returns the useful components for classification by the indicators of the performances on the different subsets of the components. Compared to traditional methods of feature selection, SVM classifiers trained on selected components show improved classification performance and a reduction in computational overhead.

scholarly journals Analysing the Data on Incomes in the Regional Context by the Principal Component Method

2019 ◽  
Vol 15 (2) ◽  
pp. 601-617
Author(s):  
B G. Ilyasov ◽  
E.A. Makarova ◽  
E.S. Zakieva ◽  
E.S. Gizdatullina
Keyword(s):  
Principal Component ◽  
Principal Component Method ◽  
Regional Context ◽  
Component Method
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