METHOD FOR PRE-CLASSIFICATION OF HIGH-DIMENSIONAL CHARACTERISTIC VECTORS FOR THE PURPOSES OF SIGNAL PROCESSING

摘要

A preclassification process of high-dimensional characteristic vectors for signal processing purposes is disclosed. In a first data reduction step, the dimensions of the characteristic vectors are reduced, using a neural algorithm that allows particularly quick learning the weighting values to be achieved. In a second step of the process, the low-dimensional characteristic vectors are grouped into individual clusters. Thanks to a special fusion procedure of the individual clusters, the number of required spacing calculations is minimal. This process is particularly suitable for pre-classifying biomedical data.