Matrix factorization and the procedure of data fusion are used to detect patterns in data. The factorized model maps the data to a low-dimensional space, therefore shrinking it and partially eliminating noise. Factorized models
are thus more robust and have a higher predictive accuracy. With this procedure we could solve the problem of overfitting in neural networks and improve their ability to generalize. Here, we report on how to simultaneously
factorize the parameters of a neural network, which can be represented with multiple matrices, to prune not important connections and therefore improve predictive accuracy. We report on empirical results of pruning normal and
deep neural networks. The proposed method performs similarly to the best standard approaches to pruning neural networks.