Multivariate Structural Bernoulli Mixtures for Recognition of Handwritten Numerals

As shown recently, the structural optimization of probabilistic neural networks can be included into EM algorithm by introducing a special type of multivariate Bernoulli mixtures. However, the underlying loglikelihood criterion is known to be multimodal in case of mixtures and therefore the EM iteration process may be starting-point dependent. In the present paper we discuss the possibility of a proper initialization of EM algorithm by means of a nonparametric optimally smoothed kernel estimate of the unknown probability distribution. The method has been applied to recognize unconstrained handwritten numerals from the database of Concordia University in Montreal.