A new learning approach based on equidistortion principle for optimal vector quantizer design

The authors theoretically derive a basic principle called the equidistortion principle for the design of optimal vector quantizers. This principle can be regarded as a extension of Gersho's theory (1979). A new learning algorithm is presented with a selection mechanism based on this principle. Since no probabilistic model is assumed in deriving the principle, the associated algorithm, unlike conventional algorithms, can minimize distortion without a particular initialization procedure, even when the input data cluster in a number of regions in the input vector space. The optimality of the algorithm is demonstrated and the experimental results on real speech data are shown.<<ETX>>