Optimization Effects for Word Representations with L2-Regularized Non-Parametric Model for Contrasting Epochs

The high dimensional sparse matrix word representations applied with supervised non-parametric learning models can have some infractions from training the model and result in errors known as loss. This loss of information can be retained using mathematical functions known as Optimizers which calculate the partial derivatives of the model parameters to yield better results. In this paper we have presented a result oriented experimentation of loss convergence performed on 5000 dimensional sparse matrix word representation with L2 regularized non-parametric supervised text classification model. We have presented results over 10, 50 and 100 epochs of training and traced the optimization prowess of varied loss optimizers that use different convergence approaches.