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• W1806551898 abstract "In this work we study the validity of the so-called curse of dimensionality for indexing of databases for similarity search. We perform an asymptotic analysis, with a test model based on a sequence of metric spaces $(Omega_d)$ from which we pick datasets $X_d$ in an i.i.d. fashion. We call the subscript $d$ the dimension of the space $Omega_d$ (e.g. for $mathbb{R}^d$ the dimension is just the usual one) and we allow the size of the dataset $n=n_d$ to be such that $d$ is superlogarithmic but subpolynomial in $n$. We study the asymptotic performance of pivot-based indexing schemes where the number of pivots is $o(n/d)$. We pick the relatively simple cost model of similarity search where we count each distance calculation as a single computation and disregard the rest. We demonstrate that if the spaces $Omega_d$ exhibit the (fairly common) concentration of measure phenomenon the performance of similarity search using such indexes is asymptotically linear in $n$. That is for large enough $d$ the difference between using such an index and performing a search without an index at all is negligeable. Thus we confirm the curse of dimensionality in this setting." @default.
• W1806551898 created "2016-06-24" @default.
• W1806551898 creator A5024463245 @default.
• W1806551898 date "2009-05-13" @default.
• W1806551898 modified "2023-09-27" @default.
• W1806551898 title "Curse of Dimensionality in the Application of Pivot-based Indexes to the Similarity Search Problem" @default.
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• W1806551898 doi "https://doi.org/10.20381/ruor-19107" @default.
• W1806551898 hasPublicationYear "2009" @default.
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