Dimension Estimation

Dimension estimation aims to determine the intrinsic dimensionality of data, representing the minimal number of variables needed to effectively describe a system, even when embedded in a higher-dimensional space. Current research focuses on improving the robustness and accuracy of estimation methods, particularly in the presence of noise, curvature, and limited data, employing techniques like local PCA adaptations, likelihood-based approaches, and adversarial methods. These advancements are crucial for various applications, including dimensionality reduction, anomaly detection, and even distinguishing between human-generated and AI-generated text, highlighting the broad impact of accurate dimension estimation across diverse fields.