Mahalanobis Distance

Mahalanobis distance is a statistical measure quantifying the distance between a point and a distribution, considering the covariance structure of the data. Current research focuses on leveraging Mahalanobis distance for improved performance in various machine learning tasks, including anomaly detection, out-of-distribution detection, and clustering, often within the context of deep learning models and optimal transport frameworks. This adaptable metric enhances model robustness and accuracy across diverse applications such as medical image analysis, crowd localization, and point cloud registration, offering significant improvements over traditional Euclidean distance-based methods.