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2012
Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Groth, D. +3 more
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Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Groth, D. +3 more
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From Principal Curves to Granular Principal Curves
IEEE Transactions on Cybernetics, 2014Principal curves arising as an essential construct in dimensionality reduction and data analysis have recently attracted much attention from theoretical as well as practical perspective. In many real-world situations, however, the efficiency of existing principal curves algorithms is often arguable, in particular when dealing with massive data owing to
Hongyun, Zhang +3 more
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Decrease of Principal-Principall Conflicts [PDF]
SSRN Electronic Journal, 2015 Most of analyses for corporate governance have a company with dispersed ownership as a research object. Relevant to this type of company classical conflict „principal-agent“ is decide by traditional mechanisms of corporate governance and mainly by internal mechanisms.
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2014
Long entry in "Encyclopedia of Quality of Life and Well-Being Research"
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Long entry in "Encyclopedia of Quality of Life and Well-Being Research"
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Invigorating principal/assistant principal relationships
Phi Delta KappanRelationships between and among school administrative team members are critical components of school culture. They serve as a guidepost for the behaviors and attitudes of staff and students. To invigorate these relationships, three embodied practices have high yield not only for the relationships among administrators, but also for fostering a healthy ...
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Principal components or principal axes
1999The expression principal components first appeared in the writings of the American statistician Harold Hotelling in 1933, but the technique was known earlier as principal axes and goes back to Karl Pearson (1901). In principle, principal components are appropriate for the analysis of variation within a sample coming from a single statistical population
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The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 1945
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