Prediction and classification in nonlinear data analysis: Something old, something new, something borrowed, something blue
- Cite this article as:
- Meulman, J.J. Psychometrika (2003) 68: 493. doi:10.1007/BF02295607
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Prediction and classification are two very active areas in modern data analysis. In this paper, prediction with nonlinear optimal scaling transformations of the variables is reviewed, and extended to the use of multiple additive components, much in the spirit of statistical learning techniques that are currently popular, among other areas, in data mining. Also, a classification/clustering method is described that is particularly suitable for analyzing attribute-value data from systems biology (genomics, proteomics, and metabolomics), and which is able to detect groups of objects that have similar values on small subsets of the attributes.