The critical role of hyperparameter tuning in machine learning: A focus on the SVD method for matrix completion

In machine learning, the determination of hyperparameters plays an essential role.The significant impact of these parameters on the accuracy of algorithms across problem-solving scenarios cannot be denied.Improper selection of values can significantly increase errors and affects outcomes.Low rank matrix completion, an optimization problem to recover and complete a partial matrix, is an example of dealing with hyperparameter tuning.Based on the experimental knowledge, we find that establishing values for hyperparameters is imperative to achieve an optimal solution to this problem.This study investigates the hyperparameter determination of the singular value thresholding (SVT) method and proposes an approach for selecting these parameters to attain superior solutions.