Dimension reduction, feature extraction and interpretation of data with network computing

The subject matter of this paper is one of long-standing interest to the Pattern Recognition and Artificial Intelligence research communities, namely that of ''feature extraction'' for facilitating the task of classification or various other tasks. We show that internal representations of neural networks do not yield unique feature values but can provide the basis for facilitating a number of useful information management tasks, such as memorization, categorization, discovery, associative recall and others. These matters are illustrated with three sets of data, one of a benchmark nature, another of the nature of real-world sensor data, and a third set consisting of semiconductor crystal structure parameters.