Clustering in dimension reduction for function approximation problem

Abstract

In order to reduce the dimension of input vectors before construction of ap-proximation MAVE-type methods (minimum average variance estimation) can be used, however they are very computationally intensive. In the present work the modification of method MAVE is described which allows substantial decrease of algorithm run time at the expense of small error increase.