Distance Distribution

Distance distribution analysis focuses on quantifying the spatial relationships within datasets to reveal underlying structure, such as clusters or anomalies. Current research emphasizes developing novel methods for measuring and interpreting these distributions, including entropy-based approaches for cluster analysis and graph-based techniques coupled with distributional distances (like Wasserstein distance) for anomaly detection in areas such as adversarial attacks on neural networks and time series analysis. These advancements improve the ability to analyze complex, high-dimensional data without relying solely on pre-defined labels, offering valuable tools for various scientific and engineering applications. The use of cumulative Radon features and other shallow features combined with efficient distance measures is also gaining traction for improved performance in anomaly detection.