Principal component variable discriminant plots: A novel approach for interpretation and analysis of multi-class data
| Autor: | Nils B. Vogt |
|---|---|
| Rok vydání: | 1988 |
| Předmět: |
business.industry
Applied Mathematics Sparse PCA Pattern recognition Variance (accounting) Covariance Linear discriminant analysis Kernel principal component analysis Analytical Chemistry Discriminant Multiple correspondence analysis Statistics Principal component analysis Artificial intelligence business Mathematics |
| Zdroj: | Journal of Chemometrics. 2:81-84 |
| ISSN: | 1099-128X 0886-9383 |
| DOI: | 10.1002/cem.1180020109 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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