In this communication, we propose a novel approach to perform the unsupervised and non parametric clustering of n-D data upon a Bayesian framework. The iterative approach developed is derived from the Classification Expectation-Maximization (CEM) algorithm, in which the parametric modelling of the mixture density is replaced by a non parametric modelling using local kernels, and the posterior probabilities account for the coherence of current clusters through the measure of class-conditional entropies. Applications of this method to synthetic and real data including multispectral images are presented. The classification issues are compared with other recent unsupervised approaches, and we show that our method reaches a more reliable estimation of the number of clusters while providing slightly better rates of correct classification in average.
|Teitl||Proceedings of the International Conference on signal processing and multimedia applications (SIGMAP)|
|Cyhoeddwr||Institute for Systems and Technologies of Information, Control and Communication Press|
|Nifer y tudalennau||8|
|Statws||Cyhoeddwyd - 2007|