Sparse Learning

Sparse Learning is a method aimed at extracting sparse representations from high-dimensional data by optimizing model parameters to make most of the weights close to zero, thereby achieving feature selection and dimensionality reduction. Its core objective is to enhance the interpretability and computational efficiency of the model while maintaining or improving predictive performance. Sparse Learning has significant application value in fields such as machine learning, signal processing, and statistics, especially when dealing with large-scale, high-dimensional datasets, as it can effectively reduce the risk of overfitting and improve model generalization.