Hierarchies of Autoassociators

The Principal Component Pyramid is a hierarchical neural network which can successfully be employed in image compression and feature extraction of images. Previously, the construction of the network from the corresponding pyramid was done on a case by case basis. In this paper we automate this process by giving formulas describing the size of the network and the number of weight constraints in the net. 1. Introduction The deficiencies of conventional (unstructured, e.g., fully connected) neural networks become particularly apparent when a complex problem (e.g., vision) is attacked, and can be avoided by the use of properly structured networks. Leonard Uhr [10] points out that hierarchically structured networks (i.e., networks which employ only local connectivity) offer efficient solutions for many problems. Similar conclusions have been drawn by Tsotsos [9]. The particular advantage offered by hierarchical structures is that a complex problem can be decomposed into smaller subproblems..