Now showing items 1-10 of 2154
Robust principal components for hyperspectral data analysis
Remote sensing data present peculiar features and characteristics that may make their statistical processing and analysis a difficult task. Among them, it can be mentioned the volume of data involved, the redundancy, the ...
Evaluation of the growth of enterobacteria in fructooligosaccharides using the principal components analysis
(Universidade Tecnológica Federal do Paraná (UTFPR), 2016)
Consistent Principal Component Modes from Molecular Dynamics Simulations of Proteins
(American Chemical Society, 2017-04)
Principal component analysis is a technique widely used for studying the movements of proteins using data collected from molecular dynamics simulations. In spite of its extensive use, the technique has a serious drawback: ...
Scores and principal components: the relationship between components due to subjects and to variables
(Universidad Complutense de Madrid. Facultad de Psicología, 2000-05)
The main purpose of this article is: given a score matrix called S, find out the joint proportional contribution of factors due to persons (conditions, situations, and so forth) and factors due to variables, for any sij ...
A new principal component analysis-based approach for testing "similarity" of drug dissolution profiles
(Elsevier Science, 2008-05)
A new approach for testing batch "similarity" through comparison of drug dissolution profiles, based on principal component analysis with the establishment of a confidence region (PCA-CR), is presented. The dissolution ...
Gdpc: An R package for generalized dynamic principal components
(Journal Statistical Software, 2020-02-23)
Gdpc is an R package for the computation of the generalized dynamic principal components proposed in Peña and Yohai (2016). In this paper, we briefly introduce the problem of dynamical principal components, propose a ...
Application of Principal Component Analysis to Elucidate Experimental and Theoretical Information
Principal Component Analysis has been widely used in different scientific areas and fordifferent purposes. The versatility and potentialities of this unsupervised method for dataanalysis, allowed the scientific community ...
Robust nonlinear principal components
All known approaches to nonlinear principal components are based on minimizing a quadratic loss, which makes them sensitive to data contamination. A predictive approach in which a spline curve is fit minimizing a residual ...