Principal component variable discriminant plots: A novel approach for interpretation and analysis of multi-class data
✍ Scribed by Nils B. Vogt
- Publisher
- John Wiley and Sons
- Year
- 1988
- Tongue
- English
- Weight
- 271 KB
- Volume
- 2
- Category
- Article
- ISSN
- 0886-9383
No coin nor oath required. For personal study only.
✦ Synopsis
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 variancekovariance decomposition (class models) may be used to investigate and interpret the data-structure of separate classes. The potential of comparing the loadings of variables o n subsequent eigenvectors in two class models where the same variables have been used will give information for determining how the variancekovariance 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.