Applying standard dimensionality reduction techniques, we show how to perform approximate range searching in higher dimension while avoiding the curse of ...
Approximate range searching in higher dimension - ScienceDirect
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Applying standard dimensionality reduction techniques, we show how to perform approximate range searching in higher dimension while avoiding the curse of ...
Applying standard dimensionality reduction techniques, we show how to perform approximate range searching in higher dimension while avoiding the curse of ...
In this paper we show that if one is willing to allow approximate ranges, then it is possible to do much better.
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Applying standard dimensionality reduction techniques, we show how to perform approximate range searching in higher dimension while avoiding the curse of ...
Apr 22, 2011 · The most popular is Locality-Sensitive Hashing (LSH), which maps a set of points in a high-dimensional space into a set of bins, ie, a hash table.
Applying standard dimensionality reduction techniques, we show how to perform approximate range searching in higher dimension while avoiding the curse of ...
This work considers sets of ranges consisting of general convex bodies, axis-aligned rectangles, halfspaces, Euclidean balls, and simplices, and introduces ...
Jun 26, 2018 · The nearest neighbor problem is defined as follows: Given a set P of n points in some metric space (X,D), build a data structure that, given any point q, ...
Applying standard dimensionality reduction techniques, we show how to perform approximate range searching in higher dimension while avoiding the curse of ...