Dimensional Subspace

Dimensional subspace analysis focuses on identifying and leveraging lower-dimensional structures within high-dimensional data, aiming to improve efficiency and interpretability of machine learning models. Current research explores various methods for discovering these subspaces, including techniques based on linear models, neural networks (e.g., incorporating multi-head scanning and attention mechanisms), and dynamical systems. This work is significant because it addresses the "curse of dimensionality" in numerous applications, leading to improved performance and reduced computational costs in areas such as computer vision, reinforcement learning, and regression analysis.