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Locality-sensitive hashing

Known as: LSH, Locality Sensitive Hashing 
Locality-sensitive hashing (LSH) reduces the dimensionality of high-dimensional data. LSH hashes input items so that similar items map to the same… Expand
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Papers overview

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Highly Cited
2014
Highly Cited
2014
We present a new data structure for the c-approximate near neighbor problem (ANN) in the Euclidean space. For n points in Rd, our… Expand
Highly Cited
2012
Highly Cited
2012
Sign-random-projection locality-sensitive hashing (SRP-LSH) is a probabilistic dimension reduction method which provides an… Expand
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Highly Cited
2011
Highly Cited
2011
Locality-sensitive hashing (LSH) is a basic primitive in several large-scale data processing applications, including nearest… Expand
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Highly Cited
2010
Highly Cited
2010
Non-metric distances are often more reasonable compared with metric ones in terms of consistency with human perceptions. However… Expand
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Highly Cited
2010
Highly Cited
2010
It is well known that high-dimensional nearest neighbor retrieval is very expensive. Dramatic performance gains are obtained… Expand
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Highly Cited
2009
Highly Cited
2009
  • B. Kulis, K. Grauman
  • IEEE 12th International Conference on Computer…
  • 2009
  • Corpus ID: 7201154
Fast retrieval methods are critical for large-scale and data-driven vision applications. Recent work has explored ways to embed… Expand
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Highly Cited
2009
Highly Cited
2009
We show how to learn a deep graphical model of the word-count vectors obtained from a large set of documents. The values of the… Expand
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Highly Cited
2008
Highly Cited
2008
This lecture note describes a technique known as locality-sensitive hashing (LSH) that allows one to quickly find similar entries… Expand
Highly Cited
2004
Highly Cited
2004
We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under lp norm, based on p… Expand
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Highly Cited
2003
Highly Cited
2003
Example-based methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality… Expand
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