A Bayesian framework for robust local intrinsic dimensionality estimation

Local Intrinsic Dimensionality (LID) is a measure of data complexity in the vicinity of a query point. In this work, we propose a novel Bayesian framework for LID estimation that improves robustness and accuracy, especially in scenarios with small neighborhood sizes ( k ≤ 10 ), where maintaining locality is critical. Our framework allows the incorporation of both informative and non-informative priors, enabling the integration of prior knowledge to enhance the estimation process. Using this framework, we derive new LID estimators and provide insights into transitional ones. Furthermore, we propose aggregation methods using linear and logarithmic pooling to combine multiple LID posteriors. These methods allow for principled integration of LID estimates across different training states of Deep Neural Networks (DNNs), such as epochs, thereby improving estimation stability and performance. We also derive a posterior predictive distribution (PPD) for modeling and synthesizing nearest-neighbor distances. Experiments demonstrate that the LID Bayesian estimation methods achieves a balanced approach between reducing variance and lowering squared bias, often leading to lower mean squared error (MSE) values for small number of nearest neighbors.