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Principal Component Analysis [PDF]
Transfusion, 2018 Principal component analysis (PCA) is often applied for analyzing data in the most diverse areas. This work reports, in an accessible and integrated manner, several theoretical and practical aspects of PCA.
Felipe L. Gewers +6 more
semanticscholar +9 more sources
Journal of the American Statistical Association, 2003 Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal component analysis.
Heng Tao Shen
semanticscholar +3 more sources
International Journal of Livestock Research, 2017 This chapter considers classical and robust principal component analysis (PCA). Principal component analysis is used to explain the dispersion structure with a few linear combinations of the original variables, called principal components.
Sidharth P. Mishra +7 more
semanticscholar +5 more sources
A robust principal component analysis
Journal of Multivariate Analysis, 1981 AbstractA robust principal component analysis for samples from a bivariate distribution function is described. The method is based on robust estimators for dispersion in the univariate case along with a certain linearization of the bivariate structure.
F.H. Ruymgaart
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Comparison Between The Method of Principal Component Analysis And Principal Component Analysis Kernel For Imaging Dimensionality Reduction [PDF]
المجلة العراقية للعلوم الاحصائية, 2019 This paper tackles with two methods to dimensionality reduction, namely principal component analysis (PCA ) in the case of linear combinations and kernel principal component analysis method in the case of nonlinear combinations to digital image ...
Assel Muslim Essa, Asmaa Ghalib Alrawi
doaj +1 more source
Principal component analysis in pig breeds identification
Indian Journal of Animal Sciences, 2023 Maintaining the purity of pig breeds is an essential task for their economic value. The traditional breed identification methods through coat colour are prone to error due to huge intra-breed variation. This paper uses principal component Analysis (PCA)
SANKET DAN +4 more
doaj +1 more source
Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018 In this paper, we consider the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum.
Canyi Lu +5 more
semanticscholar +1 more source
Reionization constraints using Principal Component Analysis [PDF]
, 2005 Using a semi-analytical model developed by Choudhury & Ferrara (2005) we study the observational constraints on reionization via a principal component analysis (PCA).
Mitra, Sourav +3 more
core +7 more sources
Principal component analysis: a review and recent developments
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2016 Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss.
I. Jolliffe, J. Cadima
semanticscholar +1 more source
Modal Principal Component Analysis [PDF]
Neural Computation, 2020 Principal component analysis (PCA) is a widely used method for data processing, such as for dimension reduction and visualization. Standard PCA is known to be sensitive to outliers, and various robust PCA methods have been proposed. It has been shown that the robustness of many statistical methods can be improved using mode estimation instead of mean ...
Hideitsu Hino, Keishi Sando
openaire +3 more sources