AI RESEARCH
On the Use of Bagging for Local Intrinsic Dimensionality Estimation
arXiv CS.LG
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ArXi:2603.24384v1 Announce Type: new The theory of Local Intrinsic Dimensionality (LID) has become a valuable tool for characterizing local complexity within and across data manifolds, ing a range of data mining and machine learning tasks. Accurate LID estimation requires samples drawn from small neighborhoods around each query to avoid biases from nonlocal effects and potential manifold mixing, yet limited data within such neighborhoods tends to cause high estimation variance.