19,241 research outputs found
Analysis of approximate nearest neighbor searching with clustered point sets
We present an empirical analysis of data structures for approximate nearest
neighbor searching. We compare the well-known optimized kd-tree splitting
method against two alternative splitting methods. The first, called the
sliding-midpoint method, which attempts to balance the goals of producing
subdivision cells of bounded aspect ratio, while not producing any empty cells.
The second, called the minimum-ambiguity method is a query-based approach. In
addition to the data points, it is also given a training set of query points
for preprocessing. It employs a simple greedy algorithm to select the splitting
plane that minimizes the average amount of ambiguity in the choice of the
nearest neighbor for the training points. We provide an empirical analysis
comparing these two methods against the optimized kd-tree construction for a
number of synthetically generated data and query sets. We demonstrate that for
clustered data and query sets, these algorithms can provide significant
improvements over the standard kd-tree construction for approximate nearest
neighbor searching.Comment: 20 pages, 8 figures. Presented at ALENEX '99, Baltimore, MD, Jan
15-16, 199
Efficient Large-scale Approximate Nearest Neighbor Search on the GPU
We present a new approach for efficient approximate nearest neighbor (ANN)
search in high dimensional spaces, extending the idea of Product Quantization.
We propose a two-level product and vector quantization tree that reduces the
number of vector comparisons required during tree traversal. Our approach also
includes a novel highly parallelizable re-ranking method for candidate vectors
by efficiently reusing already computed intermediate values. Due to its small
memory footprint during traversal, the method lends itself to an efficient,
parallel GPU implementation. This Product Quantization Tree (PQT) approach
significantly outperforms recent state of the art methods for high dimensional
nearest neighbor queries on standard reference datasets. Ours is the first work
that demonstrates GPU performance superior to CPU performance on high
dimensional, large scale ANN problems in time-critical real-world applications,
like loop-closing in videos
Fast -NNG construction with GPU-based quick multi-select
In this paper we describe a new brute force algorithm for building the
-Nearest Neighbor Graph (-NNG). The -NNG algorithm has many
applications in areas such as machine learning, bio-informatics, and clustering
analysis. While there are very efficient algorithms for data of low dimensions,
for high dimensional data the brute force search is the best algorithm. There
are two main parts to the algorithm: the first part is finding the distances
between the input vectors which may be formulated as a matrix multiplication
problem. The second is the selection of the -NNs for each of the query
vectors. For the second part, we describe a novel graphics processing unit
(GPU) -based multi-select algorithm based on quick sort. Our optimization makes
clever use of warp voting functions available on the latest GPUs along with
use-controlled cache. Benchmarks show significant improvement over
state-of-the-art implementations of the -NN search on GPUs
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