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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© 2011 Springer-Verlag GmbH Berlin Heidelberg
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Andoni, A. (2011). Nearest Neighbor Search in High-Dimensional Spaces. In: Murlak, F., Sankowski, P. (eds) Mathematical Foundations of Computer Science 2011. MFCS 2011. Lecture Notes in Computer Science, vol 6907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22993-0_1
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DOI: https://doi.org/10.1007/978-3-642-22993-0_1
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