Bayesian Ying-Yang machine, clustering and number of clusters

It is shown that a particular case of the Bayesian Ying–Yang learning system and theory reduces to the maximum likelihood learning of a finite mixture, from which we have obtained not only the EM algorithm for its parameter estimation Ž and its various approximate but fast algorithms for clustering in general cases including Mahalanobis distance clustering or . elliptic clustering , but also criteria for the selection of the number of densities in a mixture, and the number k in the conventional Mean Square Error clustering. Moreover, a Re-weighted EM algorithm is also proposed and shown to be more robust in learning. Finally, experimental results are provided. q 1997 Elsevier Science B.V.