In the context of optimizing a Linear Regression model for supply management across a sales network, you are faced with a dataset comprising a vast array of driving factors. The model's current performance is hindered by the high dimensionality of the feature space, leading to inefficiencies in both training and inference times. Your objective is to reduce the number of features without significantly compromising the model's predictive accuracy. Considering the need for computational efficiency and the preservation of valuable information, which of the following techniques would be most appropriate? (Choose one correct option)