Sparsity Inducing

Sparsity-inducing techniques aim to find solutions with minimal non-zero elements, improving efficiency and interpretability in various machine learning problems. Current research focuses on developing novel regularizers and algorithms, particularly within the context of low-rank matrix and tensor completion, hyperspectral unmixing, and deep neural network training, often employing methods like iterative hard thresholding and alternating direction method of multipliers. These advancements lead to improved performance in applications ranging from image reconstruction and social network analysis to robust matrix completion and efficient deep learning model training.