The local intrinsic dimensionality (LID) model enables assessment of the complexity of the local neighbourhood around a specific query object of interest. In this paper, we study variations in the LID of a query, with respect to different subspaces and local neighbourhoods. We illustrate the surprising phenomenon of how the LID of a query can substantially decrease as further features are included in a dataset. We identify the role of two key feature properties in influencing the LID for feature combinations: correlation and dominance. Our investigation provides new insights into the impact of different feature combinations on local regions of the data.
Hashem, T., Rashidi, L., Bailey, J., & Kulik, L. (2019). Characteristics of Local Intrinsic Dimensionality (LID) in Subspaces: Local Neighbourhood Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11807 LNCS, pp. 247–264). Springer. https://doi.org/10.1007/978-3-030-32047-8_22