scholarly journals Higher order principal component analysis of eigen values with special structures covariance matrices

2020 ◽  
Author(s):  
S. Kalaivani ◽  
K. Sivakumar ◽  
S. Balamuralitharan
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Higher Order ◽  
Covariance Matrices ◽  
Eigen Values

scholarly journals Principal Component Analysis of Some Quantitative and Qualitative Traits in Iranian Spinach Landraces

2017 ◽  
Vol 71 (4) ◽  
pp. 307-310
Author(s):  
Mehdi Mohebodini ◽  
Naser Sabaghnia ◽  
Farhad Behtash ◽  
Mohsen Janmohammadi
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Quantitative Traits ◽  
Agronomic Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Leaf Width ◽  
Petiole Length ◽  
Qualitative Traits ◽  
Eigen Values

Abstract Landraces of spinach in Iran have not been sufficiently characterised for their morpho-agronomic traits. Such characterisation would be helpful in the development of new genetically improved cultivars. In this study 54 spinach accessions collected from the major spinach growing areas of Iran were evaluated to determine their phenotypic diversity profile of spinach genotypes on the basis of 10 quantitative and 9 qualitative morpho-agronomic traits. High coefficients of variation were recorded in some quantitative traits (dry yield and leaf area) and all of the qualitative traits. Using principal component analysis, the first four principal components with eigen-values more than 1 contributed 87% of the variability among accessions for quantitative traits, whereas the first four principal components with eigen-values more than 0.8 contributed 79% of the variability among accessions for qualitative traits. The most important relations observed on the first two principal components were a strong positive association between leaf width and petiole length; between leaf length and leaf numbers in flowering; and among fresh yield, dry yield and petiole diameter; a near zero correlation between days to flowering with leaf width and petiole length. Prickly seeds, high percentage of female plants, smooth leaf texture, high numbers of leaves at flowering, greygreen leaves, erect petiole attitude and long petiole length are important characters for spinach breeding programmes.

scholarly journals Dimensionality Reduction of Dynamic Mesh Animations Using HO-SVD

2013 ◽  
Vol 3 (4) ◽  
pp. 277-289 ◽  
Author(s):  
Michał Romaszewski ◽  
Piotr Gawron ◽  
Sebastian Opozda
Keyword(s):  
Principal Component Analysis ◽  
Singular Value Decomposition ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Higher Order ◽  
Singular Value ◽  
3D Mesh ◽  
Reduction Of Dimensionality ◽  
Value Decomposition

Abstract This work presents an analysis of Higher Order Singular Value Decomposition (HOSVD) applied to reduction of dimensionality of 3D mesh animations. Compression error is measured using three metrics (MSE, Hausdorff, MSDM). Results are compared with a method based on Principal Component Analysis (PCA) and presented on a set of animations with typical mesh deformations.

scholarly journals Identification of informative performance traits in swine using principal component analysis

2005 ◽  
Vol 57 (6) ◽  
pp. 805-810 ◽  
Author(s):  
L. Barbosa ◽  
P.S. Lopes ◽  
A.J. Regazzi ◽  
S.E.F. Guimarães ◽  
R.A. Torres
Keyword(s):  
Principal Component Analysis ◽  
Litter Size ◽  
Average Daily Gain ◽  
Principal Component ◽  
Original Data ◽  
Relevant Information ◽  
Component Analysis ◽  
Data Set ◽  
Performance Traits ◽  
Eigen Values

Using principal component analysis, records of 435 animals from an F2 swine population were used to identity independent and informative variables of economically important performance. The following performance traits were recorded: litter size at birth (BL), litter size at weaning (WL), teat number (TN), birth weight (BW), weight at 21 (W21), 42 (W42), 63 (W63) and 77 (W77) days of age, average daily gain (ADG), feed intake (FI) and feed:gain ratio (FGR) from 77 to 105 days of age. Six principal components expressed variation lower than 0.7 (eigen values lower than 0.7) suggesting that six variables could be discarded with little information loss. The discarded variables present significant simple linear correlation with the retained variables. Retaining variables BL, TN, W77, FI and FGR and eliminating all the rest would retain most of the relevant information in the original data set.

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