Dynamic selection of model parameters in principal components analysis neural networks

One of the best known techniques for multidimensional data analysis is the Principal Components Analysis (PCA). A number of local PCA neural models have been proposed to partition an input distribution into meaningful clusters. Each neuron of these models uses a certain number of basis vectors to represent the principal directions of a particular cluster. Most of these neural networks are unable to learn the number of basis vectors, which is specified a priori as a fixed parameter. This leads to poor adaptation to input data. Here we develop a method where the number of basis vectors of each neuron is learned. Then we apply this method to a well known local PCA neural model. Finally, experimental results are presented where the original and modified versions of the neural model are compared.