Add to ORKGAbstract—Error Weighted Hashing (EWH) is an efficient algorithm for Approximate k-Nearest neighbour search in Ham-ming space. It is more efficient than Locality sensitive Hashing algorithm (LSH) as it generates shorter list of strings for finding exact distance from the querry. Parallelization strategies for EWH algorithm using CUDA are discussed. The results are compared with distributed LSH algorithm proposed in [2]. Index Terms—Hamming space, Nearest neighbour search I
Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest neighbo...
Many modern applications of AI such as web search, mobile browsing, image processing, and natural la...
Recently, hashing techniques have been widely applied to solve the approximate nearest neighbors sea...
We present an efficient GPU-based parallel LSH algorithm to perform approximate k-nearest neighbor c...
Locality Sensitive Hashing (LSH) is widely recognized as one of the most promising approaches to sim...
We present a new Bi-level LSH algorithm to perform approximate k-nearest neighbor search in high dim...
It is well known that high-dimensional nearest-neighbor retrieval is very expensive. Many signal pro...
A method is proposed for indexing spaces with arbitrary distance measures, so as to achieve efficien...
International audienceIt is well known that high-dimensional nearest-neighbor retrieval is very expe...
International audienceLocality-Sensitive Hashing (LSH) is widely used to solve approximate nearest n...
In this paper we present an original approach for finding approximate nearest neighbours in collecti...
Many modern applications of AI such as web search, mobile browsing, image processing, and natural la...
Approximate Nearest Neighbor (ANN) search in high dimensional space has become a fundamental paradig...
Recently, hashing based Approximate Nearest Neighbor (ANN) techniques have been attracting lots of a...
The nearest neighbor problem is one of the most important problems in computational geometry. Many o...
Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest neighbo...
Many modern applications of AI such as web search, mobile browsing, image processing, and natural la...
Recently, hashing techniques have been widely applied to solve the approximate nearest neighbors sea...
We present an efficient GPU-based parallel LSH algorithm to perform approximate k-nearest neighbor c...
Locality Sensitive Hashing (LSH) is widely recognized as one of the most promising approaches to sim...
We present a new Bi-level LSH algorithm to perform approximate k-nearest neighbor search in high dim...
It is well known that high-dimensional nearest-neighbor retrieval is very expensive. Many signal pro...
A method is proposed for indexing spaces with arbitrary distance measures, so as to achieve efficien...
International audienceIt is well known that high-dimensional nearest-neighbor retrieval is very expe...
International audienceLocality-Sensitive Hashing (LSH) is widely used to solve approximate nearest n...
In this paper we present an original approach for finding approximate nearest neighbours in collecti...
Many modern applications of AI such as web search, mobile browsing, image processing, and natural la...
Approximate Nearest Neighbor (ANN) search in high dimensional space has become a fundamental paradig...
Recently, hashing based Approximate Nearest Neighbor (ANN) techniques have been attracting lots of a...
The nearest neighbor problem is one of the most important problems in computational geometry. Many o...
Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest neighbo...
Many modern applications of AI such as web search, mobile browsing, image processing, and natural la...
Recently, hashing techniques have been widely applied to solve the approximate nearest neighbors sea...