The purpose of this research is to study technical aspects involved in the implementation of a Principal Component Analysis (PCA) neural network in terms of predictive capacity, generalization and accuracy in order to establish optimal criteria for the validation and implementation thereof. Our hypothesis is that the statistical structure of the data affects the optimal performance of a PCA neural network in the unsupervised context. It was demonstrated that the Hebbian algorithm at the learning phase ensures enhanced quality of network representation as it makes efficient use of information where generalized variance is large. 

The purpose of this research is to study technical aspects involved in the implementation of a Principal Component Analysis (PCA) neural network in terms of predictive capacity, generalization and accuracy in order to establish optimal criteria for the validation and implementation thereof. Our hypothesis is that the statistical structure of the data affects the optimal performance of a PCA neural network in the unsupervised context. It was demonstrated that the Hebbian algorithm at the learning phase ensures enhanced quality of network representation as it makes efficient use of information where generalized variance is large.