Average Competitive Learning Vector Quantization

Abstract

We propose a new algorithm for vector quantization:Average Competitive Learning Vector Quantization (ACLVQ). It is a rather simple modification of the classical Competitive Learning Vector Quantization (CLVQ). This new formulation give us similar results for the quantization error to those obtained by the CLVQ and reduce considerably the computation time to achieve the optimal quantizer. We establish the convergence of the method via the Kushner–Clark approach, and compare the two algorithms via the Central Limit Theorem. A simulation study is carried out showing the good performance of our proposal.