On a novel unsupervised competitive learning algorithm for scalar quantization
Marc M. Van Hulle, Dominique Martinez · IEEE Transactions on Neural Networks · 1994
This letter presents a novel unsupervised competitive learning rule called the boundary adaptation rule (BAR), for scalar quantization. It is shown both mathematically and by simulations that BAR converges to equiprobable quantizations of univariate probability density functions and that, in this way, it outperforms other unsupervised competitive learning rules.