Sparse Subspace

Sparse subspace methods aim to represent high-dimensional data using lower-dimensional subspaces, improving efficiency and interpretability while mitigating issues like overfitting and the curse of dimensionality. Current research focuses on developing algorithms like Meta Subspace Pursuit and unfolding ADMM for enhanced subspace clustering, particularly in applications such as hyperspectral image analysis and Bayesian neural networks. These advancements are significant because they enable efficient processing of large datasets, improve the robustness of machine learning models, and offer enhanced uncertainty quantification, leading to more reliable and accurate results in various fields.