Premium
Principal component variable discriminant plots: A novel approach for interpretation and analysis of multi‐class data
Author(s) -
Vogt Nils B.
Publication year - 1988
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180020109
Subject(s) - principal component analysis , linear discriminant analysis , mathematics , covariance , pattern recognition (psychology) , class (philosophy) , discriminant , artificial intelligence , variance (accounting) , functional principal component analysis , covariance matrix , interpretation (philosophy) , statistics , disjoint sets , computer science , accounting , business , combinatorics , programming language
Principal component analysis is a useful method for analysing data‐matrices. By analysing separate class models, i.e. disjoint principal component modelling as in the SIMCA or FCVPC programs (developed for supervised and unsupervised principal component analysis respectively), the principal component variance/covariance decomposition (class models) may be used to investigate and interpret the data‐structure of separate classes. The potential of comparing the loadings of variables on subsequent eigenvectors in two class models where the same variables have been used will give information for determining how the variance/covariance in the two datasets differ. This information may then be used either to formulate a hypothesis or to select variables which are specific for the different classes.