Overcoming the Curse of Dimensionality ?
نویسنده
چکیده
We study the behavior of pivot-based algorithms for similarity searching in metric spaces. We show that they are eeective tools for intrinsically high-dimensional spaces, and that their performance is basically dependent on the number of pivots used and the precision used to store the distances. In this paper we give a simple yet eeective recipe for practitioners seeking for a black-box method to plug in their applications. Besides, we introduce a new indexing algorithm that gives the minimum overall CPU search time for a given amount of memory, compared with other state-of-the-art approaches.
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