Low Rank Regularization

Low-rank regularization is a technique used to constrain the complexity of models by enforcing low-rank structures in their parameters, primarily aiming to improve efficiency and generalization performance. Current research focuses on developing novel regularization methods, such as those based on Haar wavelets, ranking similarity, and adaptive techniques, often integrated into matrix factorization models or deep learning architectures like scene representation networks. These advancements are impacting diverse fields, including image restoration, speech assessment, and time-series analysis, by enabling more efficient and accurate solutions for tasks involving high-dimensional data or imbalanced datasets.