Rank Metric

Rank metric research focuses on developing and applying methods that leverage the ordinal information inherent in ranked data, rather than solely relying on absolute values. Current research explores applications in diverse fields, including metric learning (e.g., using Mahalanobis distance and low-rank approximations), graph analysis (e.g., community detection via rank-based clustering), and evaluating the performance of machine learning models (e.g., using metrics like NDCG and reciprocal rank to assess the robustness of neural networks against adversarial attacks). These advancements offer improved analytical tools for various applications, providing more nuanced insights into complex systems and enhancing the evaluation of machine learning algorithms.