The choice of informative features based on heterogeneous functionals

Abstract

One of the classical problems of the theory of pattern recognition is to reduce the dimension of the original feature space from dimension N to dimension N’, N’ < N. In the first works on pattern recognition, the task was to construct an informativeness criterion that maps N-dimensional to a space of lower dimension so that a solution can be made in this space. One of the advantages of this criterion is that the classification in a space of lower dimension is faster and easier, and the criterion of being constructed once.