The weights initialization methodology of unsupervised neural networks to improve clustering stability

Abstract

A study on initialization of connection weights of neural networks is expected to be needed because various deep neural networks based on deep learning have attracted much attention recently. However, studies on the relation between the output value of the active function and the learning performance of the neural network with respect to the connection weight value have been conducted mainly on the supervised learning model. This paper focused on improving the efficiency of autonomous neural network model by studying the connection weight initialization as the neural network model of supervised learning. Adaptive resonance theory (ART) is a major model of autonomous neural network that tries to solve the stability-plasticity dilemma by using bottom-up weights and top-down weights. The conventional weights initialization method of ART was to uniformly set all weights, but the proposed method is to initialize by using pre-trained weights. Experiments show that the ART, which initializes the connectivity weights through the proposed method, performs clustering more reliably.