A study on simplified principal component analysis
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Date
2008
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Publisher
CCSHAU
Abstract
Principal component analysis though deduces dimensionality of the
data, it suffers from the draw back that the each component is a linear
combination of all the original variables and one has to interpret the result in
terms of all the original variables.
In the present study various techniques for obtaining simplified
components have been described and critically reviewed. Best linear predictor
(BLP) and corrected sum of variances (CSV) criterion have also been
presented for determining the optimality of simple components with respect
to the PCA which is considered the optimal solution. Simplified principal
components simulated and real data sets were obtained through varimax
rotation and as well as using simple component analysis algorithm proposed
by Rousson and Gasser (2004) worked out compared with the ordinary
principal components both in term of simplicity and optimality.
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Keywords
Statistical methods, Sets, Selection, Developmental stages, Solutes, Biological phenomena, Physical control, Byproducts, Accounts, Planting