2019/07/15 by Ruben Becker, Becker, Ruben, Imane Hafnaoui +7 · 1 citation
Computer Science · Engineering · #Advanced Numerical Analysis Techniques #FOS: Computer and information sciences #Image and Object Detection Techniques #Industrial Vision Systems and Defect Detection #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.1907.06771
openalex publication_date 2019/07/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Axis-aligned subspace clustering generally entails searching through enormous numbers of subspaces (feature combinations) and evaluation of cluster quality within each subspace. In this paper, we tackle the problem of identifying subsets of features with the most significant contribution to the formation of the local neighborhood surrounding a given data point. For each point, the recently-proposed Local Intrinsic Dimension (LID) model is used in identifying the axis directions along which features have the greatest local discriminability, or equivalently, the fewest number of components of LID that capture the local complexity of the data. In this paper, we develop an estimator of LID along axis projections, and provide preliminary evidence that this LID decomposition can indicate axis-aligned data subspaces that support the formation of clusters.