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Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Efficient search for approximate nearest neighbor in high dimensional spaces
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We study the so called combinatorial framework for algorithmic problems in similarity spaces. Namely, the input dataset is represented by a comparison oracle that given three points x, y, y' answers whether y or y' is closer to x. We assume that the similarity order of the dataset satisfies the four variations of the following disorder inequality: if x is the a'th most similar object to y and y is the b'th most similar object to z, then x is among the D(a + b) most similar objects to z, where D is a relatively small disorder constant. Though the oracle gives much less information compared to the standard general metric space model where distance values are given, one can still design very efficient algorithms for various fundamental computational tasks. For nearest neighbor search we present deterministic and exact algorithm with almost linear time and space complexity of preprocessing, and near-logarithmic time complexity of search. Then, for near-duplicate detection we present the first known deterministic algorithm that requires just near-linear time + time proportional to the size of output. Finally, we show that for any dataset satisfying the disorder inequality a visibility graph can be constructed: all outdegrees are near-logarithmic and greedy routing deterministically converges to the nearest neighbor of a target in logarithmic number of steps. The later result is the first known work-around for Navarro's impossibility of generalizing Delaunay graphs. The technical contribution of the paper consists of handling "false positives" in data structures and an algorithmic technique up-aside-down-filter.