Wavelet optimization for classification
Marie-Françoise Lucas, C. Doncarli, Eric Hitti, Nicolas Dechamps · 2002
This paper addresses supervised signal classification using discrete time-scale representations. Given a set of learning signals and a class of discrete wavelet-basis, we propose to select the mother wavelet which yields the best classification results. This corresponds to determining the filter (used for the decomposition) that is ideally adapted to the specific classification problem at hand. It is realized by optimizing the filter coefficients according to a contrast criterion calculated on the learning set. Simulations show the efficiency of this approach.