Distance Preservation

Distance preservation in data embedding focuses on creating representations that maintain the original data's spatial relationships. Current research emphasizes developing methods that accurately preserve both local and global structures, employing techniques like autoencoders and multi-scale encoding schemes tailored to various geometries, including spherical surfaces. These advancements improve the quality and reliability of embeddings for diverse applications, ranging from graph visualization and geospatial prediction to general machine learning tasks where preserving data relationships is crucial for effective model performance. The development of robust, task-independent metrics for evaluating distance preservation is also a key area of ongoing investigation.