Euclidean Distance

Euclidean distance, the straight-line distance between two points, remains a fundamental concept in numerous scientific fields, with current research focusing on its application and limitations in high-dimensional spaces and complex data structures like covariance matrices and graphs. Researchers are exploring alternative distance metrics and developing robust algorithms, such as those based on supervised contrastive learning and adaptive weight aggregation, to improve accuracy and efficiency in various applications, including pattern recognition, clustering, and federated learning. These advancements are crucial for enhancing the performance of machine learning models and addressing challenges related to data dimensionality, noise, and malicious attacks in collaborative learning environments.