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Approximate Nearest Neighbor Search in ℓp
"... We present a new locality sensitive hashing (LSH) algorithm for capproximate nearest neighbor search in ℓp with 1 < p < 2. For a database of n points in ℓp, we achieve O(dn ρ) query time and O(dn + n 1+ρ) space, where ρ ≤ O((ln c) 2 /c p). This improves upon the previous best upper bound ρ ≤ ..."
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We present a new locality sensitive hashing (LSH) algorithm for capproximate nearest neighbor search in ℓp with 1 < p < 2. For a database of n points in ℓp, we achieve O(dn ρ) query time and O(dn + n 1+ρ) space, where ρ ≤ O((ln c) 2 /c p). This improves upon the previous best upper bound ρ
Fast Approximate Nearest Neighbor Search
"... neighbors to the query. However, nding exact l nearest neighbors to the query can be time and memory intensive [10, 9]. Hence, in some applications [10, 5] it may be acceptable to return approximate l nearest neighbors. In this work, we propose a novel fast approximate nearest neighbor search algori ..."
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neighbors to the query. However, nding exact l nearest neighbors to the query can be time and memory intensive [10, 9]. Hence, in some applications [10, 5] it may be acceptable to return approximate l nearest neighbors. In this work, we propose a novel fast approximate nearest neighbor search
Approximate nearest neighbor searching in multimedia databases
 In Proc of 17th IEEE Int. Conf. on Data Engineering (ICDE
, 2001
"... In this paper, we develop a general framework for approximate nearest neighbor queries. We categorize the current approaches for nearest neighbor query processing based on either their ability to reduce the data set that needs to be examined, or their ability to reduce the representation size of eac ..."
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Cited by 52 (12 self)
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that merges the benefits of the two general classes of approaches. Our clusterbased approach allows a user to progressively explore the approximate results with increasing accuracy. We propose a new metric for evaluation of approximate nearest neighbor searching techniques. Using both the proposed
PrototypeBased Approximate Nearest Neighbor Search
"... The principles of the prototype model for speaker recognition have been applied in a new version of the Approximate Nearest Neighbor schema. An analytical expression for the space complexity, using the prototype model, has been developed. It has also been empirically proven that increasing the error ..."
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the error bounds when using the Approximate Nearest Neighbor search speeds up the prototypebased search, with minimal reduction of search accuracy. KeyWords:  Approximate Nearest Neighbors; PrototypeBased Search; Space and Time Complexities; Speaker Recognition. 1
PrototypeBased Approximate Nearest Neighbor Search
"... Abstract: The principles of the prototype model for speaker recognition have been applied in a new version of the Approximate Nearest Neighbor schema. An analytical expression for the space complexity, using the prototype model, has been developed. It has also been empirically proven that increasin ..."
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that increasing the error bounds when using the Approximate Nearest Neighbor search speeds up the prototypebased search, with minimal reduction of search accuracy.
Composite Quantization for Approximate Nearest Neighbor Search
"... This paper presents a novel compact coding approach, composite quantization, for approximate nearest neighbor search. The idea is to use the composition of several elements selected from the dictionaries to accurately approximate a vector and to represent the vector by a short code composed of the ..."
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Cited by 4 (3 self)
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This paper presents a novel compact coding approach, composite quantization, for approximate nearest neighbor search. The idea is to use the composition of several elements selected from the dictionaries to accurately approximate a vector and to represent the vector by a short code composed
APPROXIMATE NEAREST NEIGHBOR SEARCH FOR LABELLED TREES
, 2004
"... Abstract. In many scientific areas there is a frequent need to extract a common pattern from multiple data. In most cases, however, an approximate but low cost solution is preferred to a high cost exact match. To establish a fast search engine an efficient heuristic method should be implemented. Our ..."
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. Our investigation is devoted to the approximate nearest neighbor search (ANN) for unordered labeled trees. The proposed modified bestfirst algorithm provides a O((Nq+Nb)⋅M + K⋅Nq⋅Nb/M) cost function with simple implementation details. According to our test results, realized with smaller trees where
SpaceTime Tradeoffs for Approximate Nearest Neighbor Searching
, 2009
"... Nearest neighbor searching is the problem of preprocessing a set of n point points in ddimensional space so that, given any query point q, it is possible to report the closest point to q rapidly. In approximate nearest neighbor searching, a parameter ε>0 is given, and a multiplicative error of ( ..."
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Cited by 28 (7 self)
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Nearest neighbor searching is the problem of preprocessing a set of n point points in ddimensional space so that, given any query point q, it is possible to report the closest point to q rapidly. In approximate nearest neighbor searching, a parameter ε>0 is given, and a multiplicative error
TrinaryProjection Trees for Approximate Nearest Neighbor Search
"... Abstract—We address the problem of approximate nearest neighbor (ANN) search for visual descriptor indexing. Most spatial partition trees, such as KD trees, VP trees and so on, follow the hierarchical binary space partitioning framework. The key effort is to design different partition functions (hyp ..."
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Cited by 4 (2 self)
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Abstract—We address the problem of approximate nearest neighbor (ANN) search for visual descriptor indexing. Most spatial partition trees, such as KD trees, VP trees and so on, follow the hierarchical binary space partitioning framework. The key effort is to design different partition functions
An Optimal Algorithm for Approximate Nearest Neighbor Searching in Fixed Dimensions
 ACMSIAM SYMPOSIUM ON DISCRETE ALGORITHMS
, 1994
"... Consider a set S of n data points in real ddimensional space, R d , where distances are measured using any Minkowski metric. In nearest neighbor searching we preprocess S into a data structure, so that given any query point q 2 R d , the closest point of S to q can be reported quickly. Given any po ..."
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Cited by 983 (32 self)
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Consider a set S of n data points in real ddimensional space, R d , where distances are measured using any Minkowski metric. In nearest neighbor searching we preprocess S into a data structure, so that given any query point q 2 R d , the closest point of S to q can be reported quickly. Given any
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