Norm Regularization

Norm regularization is a technique used in machine learning to constrain model complexity and improve generalization by penalizing large parameter values, often promoting sparsity or low-rank structures. Current research focuses on developing efficient algorithms for various norm types (e.g., ℓ₀, ℓ₁, ℓ₂, ℓ₂,ₚ) within different model architectures (e.g., neural networks, support vector machines, tensor factorization), addressing challenges like non-convexity and high dimensionality. These advancements are significant for improving the performance and interpretability of machine learning models across diverse applications, including image processing, financial modeling, and continual learning.