Hierarchical vector quantisation

We present a method of vector quantisation which trades off accuracy for speed of encoding. We achieve this by hierarchically structuring a multistage encoder so that each stage encodes low dimensional input vectors. Such hierarchical encoders may easily be realised as a set of fast table look-up operations. We demonstrate how the Euclidean distortion in such a multistage encoder is approximately minimised by using Kohonen's topographic mapping learning algorithm from neural network theory. We also demonstrate the performance of the technique on various stochastic time series. We find that there is little loss in encoding accuracy, when compared with the exact nearest neighbour encoding using an equivalent single stage encoder.