Data Reduction: Principal Components Analysis

2021 ◽  
pp. 515-550
Author(s):  
James A. Middleton
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Data Reduction ◽  
Components Analysis

Effects of a Data Reduction Technique on Anthropometric Accommodation

1998 ◽  
Vol 42 (10) ◽  
pp. 727-731 ◽  
Author(s):  
Pierre Meunier
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Data Reduction ◽  
Reduction Method ◽  
Reduction Technique ◽  
Test Cases ◽  
Reduction Techniques ◽  
Components Analysis ◽  
Data Reduction Technique ◽  
Data Reduction Method

Multivariate data reduction techniques such as principal components analysis (PCA), offer the potential of simplifying the task of designing and evaluating workspaces for anthropometric accommodation of the user population. Simplification occurs by reducing the number of variables that one has to consider while retaining most, e.g. 89%, of the original dataset's variability. The error introduced by choosing to ignore some (11%) of the variability is examined in this paper. A set of eight design mannequins was generated using a data reduction method developed for MIL-STD-1776A. These mannequins, which were located on the periphery of a circle encompassing 90%, 95% and 99% of the population on two principal components, were compared with the true multivariate 90%, 95% and 99% of the population. The PCA mannequins were found to include less of the population than originally intended. The degree to which the mannequins included the true percentage of the population was found to depend mainly on the size of the initial envelope (larger envelopes were closer to the true accommodation limits). The paper also discusses some of the limitations of using limited numbers of test cases to predict population accommodation.

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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