A Principal Components Analysis of the Kaufman Assessment Battery for Children (K-ABC): Implications for the Test Results of Children with Learning Disabilities

1986 ◽  
Vol 19 (2) ◽  
pp. 80-85 ◽  
Author(s):  
James Inglis ◽  
J. S. Lawson
Keyword(s):  
Learning Disabilities ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Test Results ◽  
Assessment Battery ◽  
Components Analysis

CYTOGENETIC STUDIES OF CHROMOSOME REPLICATION IN ZEA MAYS L.: I. Analysis of quantitative autoradiographic data using Principal Components Analysis

1973 ◽  
Vol 15 (3) ◽  
pp. 417-426 ◽  
Author(s):  
George R. Douglas ◽  
David B. Walden
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Euclidean Distance ◽  
Primary Root ◽  
Autoradiographic Study ◽  
Test Results ◽  
Additional Chromosome ◽  
The Mean ◽  
Components Analysis ◽  
Silver Grains

A quantitative, autoradiographic study of 3H-TdR uptake at 18 °C was undertaken in the nuclei of primary root meristems of a highly inbred stock of maize. Plots of the mean number of silver grains over nuclei vs. hours after removal from 3H-TdR (replication profiles) revealed fluctuation in DNA replication. Wilcoxon's signed-ranks test showed that some chromosome arms had replication profiles different from the profile of the nucleus. A Principal Components Analysis (PCA) was used to characterize empirically and further compare the replication of chromosome arms. The PCA results confirm the Wilcoxon's signed-ranks test results and also suggest unique replication behavior for some additional chromosome arms. To quantitatively compare the position of individuals in the scatter diagram of the first three factor axes from the PCA, the standardized Euclidean distance of each individual from the centroid was employed. Since some chromosome arms were found to be different from the others, at least limited autonomy of replication among chromosome arms is proposed.

scholarly journals RANCANG BANGUN SISTEM PENGENALAN WAJAH DENGAN METODE PRINCIPAL COMPONENT ANALYSIS

2016 ◽  
Vol 1 (2) ◽  
pp. 59-75
Author(s):  
Salamun Salamun ◽  
Firman Wazir
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Physiological Measures ◽  
Test Results ◽  
Analysis Method ◽  
The Face ◽  
Components Analysis

The face is one of the easiest physiological measures and is often used to distinguish individual identities from one another. This facial recognition process uses raw information from pixel images generated through a camera which is then represented in the Principal Components Analysis method. The Principal Components Analysis method works by calculating the average flatvector pixel of images that have been stored in a database, from the average flatvector will get the value of each image eigenface and then the nearest eigenface value of the image will be found and then the nearest eigenface value of the image will be found the image of the face you want to recognize. The test results showed an overall success rate of face recognition of 82.27% with face data of 130 images.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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