Bayes Risk Weighted VQ and Learning VQ

This paper examines two vector quantization algorithms which can combine the tasks of compression and classiication: Bayes risk weighted vector quantization (BRVQ) proposed by Oehler et al., and Optimized Learning Vector Quantization 1 (OLVQ1) proposed by Kohonen et al. BRVQ uses a parameter to control the tradeoo between compression and clas-siication. BRVQ performance is studied for a range of values for four classiication problems. Increasing the parameter in BRVQ is intended to improve classiication performance. However, for two of the problems studied, increasing degraded clas-siication performance. A majority rule reclassiication of the nal codebook (using only the training set) greatly improves high-BRVQ performance for these cases. Finally, we compare the classiication performance and mean Square error (MSE) performance of BRVQ to that of OLVQ1 for four classiication problems. BRVQ with codebook reclassiication is found to have a lower MSE than OLVQ1 while maintaining comparable, but slightly inferior, classiication performance.