Enhanced ICA mixture model for unsupervised classification

The ICA mixture model was originally proposed to perform unsupervised classification of data modelled as a mixture of classes described by linear combinations of independent, non-Gaussian densities. Since the original learning algorithm is based on a gradient optimization technique, it was noted that its performance is affected by some known limitations associated with this kind of approach. In this paper, improvements based on implementation and modelling aspects are incorporated to ICA mixture model aiming to achieve better classification results. Comparative experimental results obtained by the enhanced method and the original one are presented to show that the proposed modifications can significantly improve the classification performance considering random generated data and the well-known iris flower data set.