Distance Based

Distance-based methods are increasingly used in machine learning for classification, clustering, and other tasks, focusing on quantifying the similarity or dissimilarity between data points or distributions. Current research explores novel distance metrics tailored to specific data types (e.g., distributions, time series, categorical variables) and integrates them into various algorithms like k-nearest neighbors and clustering techniques, often alongside dimensionality reduction methods to improve efficiency and accuracy. These advancements enhance the interpretability and computational efficiency of machine learning models across diverse fields, including astronomy, forensics, and robotics, while also providing new tools for analyzing complex data structures like social networks and biological images.

Papers