Unsupervised Neural Network Training Via a Potential Reduction Approach

Abstract

A new training algorithm based on quadratic optimization under box constraints is presented to nd the optimal weights of an unsupervised neural network architecture based on Linsker's model. Linsker's network is a multilayer network. All of the units in the network are linear. The training of Linsker's model can be reduced into a nite number of concave quadratic optimization problems. Specically at each neuron the problem is to maximize the variance of its output. We present two algorithms for this problem which are inspired by an interior point potential reduction approach to combinatorial optimization problems suggested by Warners et al. [25] Computational experience by using randomly generated instances are reported. Key words: interior point methods, potential reduction methods, quadratic programming, unsupervised learning, selforganization. The major part of the research was done while the rst author visited the department SSOR of the T.U. Delft. y The rst author gratef..