Matrix regularization

Matrix regularization refers to techniques used in machine learning and statistics to prevent overfitting and improve the generalization of models that involve matrices. In many applications, particularly in collaborative filtering, recommendation systems, and regression tasks, models use matrices to represent relationships between different entities (like users and items). Regularization helps in controlling model complexity by adding a penalty for large coefficients, hence encouraging simpler models that perform better on unseen data.