Proximity Search

Proximity search encompasses methods for identifying and analyzing the spatial relationships between data points, whether in physical space (e.g., robotics, urban planning) or abstract spaces (e.g., feature spaces in machine learning). Current research focuses on improving efficiency and accuracy through novel algorithms like dynamic octrees and proximity-aware neural networks, often incorporating multi-modal data (e.g., visual, tactile, and sensor data) for richer representations. These advancements have significant implications for diverse fields, enhancing applications ranging from human-robot collaboration and traffic accident prediction to personalized medicine and federated learning.