Principal component analysis on interval data
The main ideas of principal component analysis (PCA) for multivariate interval-valued observations are discussed. Concepts of interval eigenvalues and singular values, interval eigenvectors, and interval correlation matrices are discussed. The principal components for interval data are defined as interval eigenvectors of the interval correlation matrix. Representation and interpretation of interval PCA results are discussed. Analysis of an oil data set is presented as an example.
scientific article; zbMATH DE number 5158604
scientific article