matrix regularization

Matrix regularization is a technique used to prevent overfitting in machine learning models that involve matrices, such as in collaborative filtering or multi-task learning. It adds a penalty term to the model's objective function, encouraging the matrix to have certain desired properties—like being low-rank or smooth—thereby promoting simplicity and generalization. Essentially, it guides the model to avoid over-complicating patterns, ensuring that the learned relationships are more robust and applicable to new data. This helps improve the model's performance and prevent it from fitting noise in the training data.