Ranked Modelling with Feature Selection Based on the CPL Criterion Functions

Ranked transformations should preserve a priori given ranked relations (order) between some feature vectors. Designing ranked models includes feature selection tasks. Components of feature vectors which are not important for preserving the vectors order should be neglected. This way unimportant dimensions are greatly reduced in the feature space. It is particularly important in the case of “long” feature vectors, when a relatively small number of objects is represented in a high dimensional feature space. In the paper, we describe designing ranked models with the feature selection which is based on the minimisation of convex and piecewise linear (CPL) functions.