Abstract
Nearest neighbor search in high-dimensional spaces is a ubiquitous problem in searching and analyzing massive data sets. In this problem, the goal is to preprocess a set of objects (such as images), so that later, given a new query object, one can efficiently return the object most similar to the query. This problem is of key importance in several areas, including machine learning, information retrieval, image/video/music clustering, and others. For instance, it forms the basis of a widely used classification method in machine learning: to label a new object, just find a similar but already-labeled object. Nearest neighbor search also serves as a primitive for other computational problems such as closest pair, minimum spanning tree, or variants of clustering.
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