We present an efficient GPU-based parallel LSH algorithm to perform approximate k-nearest neighbor computation in high-dimensional spaces. We use the Bi-level LSH algorithm, which can compute k-nearest neighbors with higher accuracy and is amenable to parallelization. During the first level, we use the parallel RP-tree algorithm to partition datasets into several groups so that items similar to each other are clustered together. The second level involves computing the Bi-Level LSH code for each item and constructing a hierarchical hash table. The hash table is based on parallel cuckoo hashing and Morton curves. In the query step, we use GPU-based work queues to accelerate short-list search, which is one of the main bottlenecks in LSH-based ...
Abstract — We present a novel k-nearest neighbor search algorithm (KNNS) for proximity computation i...
As the data acquisition capabilities of Earth observation (EO) satellites have been improved substa...
Locality-Sensitive Hashing (LSH) approximates nearest neighbors in high dimensions by projecting ori...
We present a new Bi-level LSH algorithm to perform approximate k-nearest neighbor search in high dim...
We demonstrate an efficient data-parallel algorithm for building large hash tables of millions of el...
International audienceIt is well known that high-dimensional nearest-neighbor retrieval is very expe...
Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest neighbo...
Finding nearest neighbors has become an important operation on databases, with applications to text ...
It is well known that high-dimensional nearest-neighbor retrieval is very expensive. Many signal pro...
The similarity search problem is found in many application domains including computer graphics, info...
Abstract—Error Weighted Hashing (EWH) is an efficient algorithm for Approximate k-Nearest neighbour ...
This paper presents an implementation of the brute-force exact k-Nearest Neighbor Graph (k-NNG) cons...
International audienceEfficiently constructing the K-Nearest Neighbor Graph (K-NNG) of large and hig...
Abstract—Similarity search is critical for many database ap-plications, including the increasingly p...
[[abstract]]Advances in non-linear dimensionality reduction provide a way to understand and visualis...
Abstract — We present a novel k-nearest neighbor search algorithm (KNNS) for proximity computation i...
As the data acquisition capabilities of Earth observation (EO) satellites have been improved substa...
Locality-Sensitive Hashing (LSH) approximates nearest neighbors in high dimensions by projecting ori...
We present a new Bi-level LSH algorithm to perform approximate k-nearest neighbor search in high dim...
We demonstrate an efficient data-parallel algorithm for building large hash tables of millions of el...
International audienceIt is well known that high-dimensional nearest-neighbor retrieval is very expe...
Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest neighbo...
Finding nearest neighbors has become an important operation on databases, with applications to text ...
It is well known that high-dimensional nearest-neighbor retrieval is very expensive. Many signal pro...
The similarity search problem is found in many application domains including computer graphics, info...
Abstract—Error Weighted Hashing (EWH) is an efficient algorithm for Approximate k-Nearest neighbour ...
This paper presents an implementation of the brute-force exact k-Nearest Neighbor Graph (k-NNG) cons...
International audienceEfficiently constructing the K-Nearest Neighbor Graph (K-NNG) of large and hig...
Abstract—Similarity search is critical for many database ap-plications, including the increasingly p...
[[abstract]]Advances in non-linear dimensionality reduction provide a way to understand and visualis...
Abstract — We present a novel k-nearest neighbor search algorithm (KNNS) for proximity computation i...
As the data acquisition capabilities of Earth observation (EO) satellites have been improved substa...
Locality-Sensitive Hashing (LSH) approximates nearest neighbors in high dimensions by projecting ori...