Rank-Based Similarity Search: Reducing the Dimensional Dependence

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Estimating Local Intrinsic Dimensionality (2015, methodology): “In addition to applications in manifold learning, measures of local ID have been used in the context of similarity search, where they are used to assess the complexity of a search query [22, 25], or to control the early termination of search [20, 21].”
Approximate Nearest Neighbor Search on High Dimensional Data - Experiments, Analyses, and Improvement (v1.0) (2016, unknown): “We evaluate Small World Graph [31] (SW(10), IS’14), Hierarchical Navigable Small World [32](HNSW(10), CoRR’16), Rank Cover Tree [23] (RCT(14), TPAMI’15), K-NN graph [16, 15] (KGraph(15), WWW’11), and our diversified proximity graph (DPG(14)).”
Fast k-Nearest Neighbour Search via Prioritized DCI (2017, background): “Examples include navigating nets (Krauthgamer & Lee, 2004), cover trees (Beygelzimer et al., 2006) and rank cover trees (Houle & Nett, 2015), which maintain sets of subsampled data points at different levels of granularity and descend through the hierarchy of neighbourhoods of decreasing radii…”
Density‐based clustering (2019, methodology): “This space-filling behavior and the associated difficulty of, for example, nearest neighbor queries has been measured by expansion models (Beygelzimer, Kakade, & Langford, 2006; Houle & Nett, 2015; Karger & Ruhl, 2002).”