Low Dimensional Subspace

Low-dimensional subspace methods aim to identify and exploit underlying low-dimensional structures within high-dimensional data, improving efficiency and performance in various machine learning tasks. Current research focuses on developing algorithms that efficiently identify these subspaces, particularly within the context of deep learning models and stochastic optimization, often employing techniques like gradient-based methods and variational Bayesian inference. These advancements have significant implications for improving the efficiency and robustness of algorithms across diverse applications, including image editing, bandit problems, and tensor completion, by reducing computational complexity and enhancing generalization.