Journal ArticleDOI
An Efficient Nearest Neighbor Search Method
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A simple, but efficient, nearest neighbor search algorithm is proposed and simulation results demonstrating its effectiveness in the case of vector quantization for a given source are presented.Abstract:
A simple, but efficient, nearest neighbor search algorithm is proposed and simulation results demonstrating its effectiveness in the case of vector quantization for a given source are presented. The simulation results indicate that use of this approach reduces the number of multiplications and additions to as low as 9 percent of those required for the conventional full search method. The reduction in the number of subtractions is also considerable. The increase in the number of comparisons is moderate, and therefore, the total number of operations can be as low as 28 percent of those required by the full search method. An additional advantage of the described algorithm is the fact that it requires no precomputations and/or extra memory.read more
Citations
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Journal ArticleDOI
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
TL;DR: In this paper, it was shown that given an integer k ≥ 1, (1 + ϵ)-approximation to the k nearest neighbors of q can be computed in additional O(kd log n) time.
Journal ArticleDOI
Quantization
Robert M. Gray,David L. Neuhoff +1 more
TL;DR: The key to a successful quantization is the selection of an error criterion – such as entropy and signal-to-noise ratio – and the development of optimal quantizers for this criterion.
Proceedings ArticleDOI
An optimal algorithm for approximate nearest neighbor searching
TL;DR: It is shown that it is possible to preprocess a set of data points in real D-dimensional space in O(kd) time and in additional space, so that given a query point q, the closest point of S to S to q can be reported quickly.
Proceedings ArticleDOI
Approximate nearest neighbor queries in fixed dimensions
Sunil Arya,David M. Mount +1 more
TL;DR: A practical variant of this algorithm is implemented, and it is shown empirically that for many point distributions this variant of the algorithm finds the nearest neighbor in moderately large dimension significantly faster than existing practical approaches.
Journal ArticleDOI
A fast nearest-neighbor algorithm based on a principal axis search tree
TL;DR: A new fast nearest-neighbor algorithm is described that uses principal component analysis to build an efficient search tree that efficiently uses a depth-first search and a new elimination criterion.
References
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Journal ArticleDOI
An Algorithm for Vector Quantizer Design
Y. Linde,A. Buzo,Robert M. Gray +2 more
TL;DR: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data.
Journal ArticleDOI
An Algorithm for Finding Best Matches in Logarithmic Expected Time
TL;DR: An algorithm and data structure are presented for searching a file containing N records, each described by k real valued keys, for the m closest matches or nearest neighbors to a given query record.
Journal Article
Vector quantization
TL;DR: During the past few years several design algorithms have been developed for a variety of vector quantizers and the performance of these codes has been studied for speech waveforms, speech linear predictive parameter vectors, images, and several simulated random processes.
Journal ArticleDOI
An Improvement of the Minimum Distortion Encoding Algorithm for Vector Quantization
Chang-da Bei,Robert M. Gray +1 more
TL;DR: A very simple method is presented for improving the efficiency of minimum distortion encoding for vector quantization by reducing the number of multiplications in a full search vector quantizer with a large number of codewords.
Journal ArticleDOI
Computational Geometry—A Survey
TL;DR: The state of the art of computational geometry is surveyed, a discipline that deals with the complexity of geometric problems within the framework of the analysis of algorithms.