A class of competitive learning models which avoids neuron underutilization problem

We study a qualitative property of a class of competitive learning (CL) models, which is called the multiplicatively biased competitive learning (MBCL) model, namely that it avoids neuron underutilization with probability one as time goes to infinity. In the MBCL, the competition among neurons is biased by a multiplicative term, while only one weight vector is updated per learning step. This is of practical interest since its instances have computational complexities among the lowest in existing CL models. In addition, in applications like classification, vector quantizer design and probability density function estimation, a necessary condition for optimal performance is to avoid neuron underutilization. Hence, it is possible to define instances of MBCL to achieve optimal performance in these applications.