Sparse Linear

Sparse linear models aim to identify and utilize only the most relevant features from high-dimensional data to build predictive models, prioritizing interpretability and efficiency. Current research focuses on improving the robustness and speed of these models, exploring techniques like Bayesian methods, expectation-maximization algorithms, and novel regularization strategies within both traditional linear frameworks and hybrid approaches incorporating deep neural networks. These advancements are impacting diverse fields, enhancing the accuracy and interpretability of models in applications ranging from drug discovery and healthcare risk assessment to time series forecasting and robust statistical testing.