K$ Distance

K-distance encompasses a family of distance metrics used in various machine learning and graph analysis tasks, aiming to improve upon limitations of traditional distance measures like sensitivity to outliers or computational cost. Current research focuses on developing robust k-distance variants, such as those based on harmonic means, partial Wasserstein distances, and regularized k-nearest neighbor approaches, often incorporating ensemble methods like bagging to enhance performance and stability. These advancements offer improved accuracy and efficiency in applications ranging from anomaly detection and clustering to classification and manifold learning, impacting fields like data mining and graph theory.