Definition
Matrix regularization extends scalar regularization concepts to matrices, often used in multi-task learning or recommendation systems. It imposes constraints on the norm of weight matrices, such as the Frobenius norm or nuclear norm, to control model complexity. This helps in reducing overfitting by discouraging large weights and can enforce low-rank structures, which is beneficial for capturing latent factors in data. It ensures that the learned representations remain stable and interpretable.
Summary
A technique applying penalty terms to matrix-valued parameters to prevent overfitting and enforce structural properties like sparsity.
Key Concepts
- Frobenius norm
- Nuclear norm
- Overfitting prevention
- Low-rank approximation
Use Cases
- Collaborative filtering
- Multi-task learning
- Dimensionality reduction