A new competitive learning algorithm for vector quantization

Abstract

In this paper, a new competitive learning algorithm based on the partial distortion theorem is proposed for the on-line vector quantizer design. The novel algorithm is called partial-distortion-equivalent competitive learning (PDECL) algorithm, which aims at making the partial distortions for each neuron (code-vector) be uniform to overcome the neuron underuse problem as well as to minimize the average distortion for the designed vector quantizer. Compared with the Kohonen learning algorithm (KLA), the frequency-sensitive competitive learning (FSCL) algorithm and the soft competition scheme (SCS) algorithm, the PDECL consistently shows the better performance than all of them and the LBG algorithm for the design of vector quantizers with different codebook sizes especially when the codebook size is large enough. >