Metric Embeddings

Metric embeddings aim to represent data points from a complex metric space in a lower-dimensional space while preserving the distances between points as accurately as possible. Current research focuses on developing efficient algorithms, such as those based on tree structures or normed spaces, to achieve low-distortion embeddings, exploring the use of neural networks (including transformers) for learning these embeddings, and analyzing the performance of different embedding methods across various data types and applications. This field is significant because effective metric embeddings enable efficient processing and analysis of high-dimensional data, finding applications in diverse areas like malware detection, graph analysis, and image processing.