The theoretical limits of the speedup that can be achieved by the dynamic pruning approach are influenced by the sparsity patterns and distribution of the latent factors in the feature matrices. The pruning rate can be optimized to balance the trade-off between speedup and prediction accuracy by considering the following factors:
Threshold Determination: The threshold values used for determining significant and insignificant latent factors play a crucial role in optimizing the pruning rate. By fine-tuning the threshold values based on the specific dataset and training process, the pruning rate can be adjusted to achieve the desired balance between speedup and prediction accuracy.
Dynamic Adjustment: The pruning rate can be dynamically adjusted during the training process based on the evolving sparsity patterns of the latent factors. By monitoring the impact of pruning on prediction accuracy and computational time, the pruning rate can be optimized to maintain a balance between speedup and accuracy.
Iterative Evaluation: Iteratively evaluating the performance of the dynamic pruning approach with different pruning rates can help identify the optimal rate that maximizes speedup while minimizing the increase in prediction error. By conducting thorough experiments and analyzing the results, the pruning rate can be fine-tuned to achieve the best trade-off between efficiency and accuracy.