Fast Approximate Nearest-Neighbor Queries in Metric Feature Spaces by Buoy Indexing

Volmer, Stephan

doi:10.1007/3-540-45925-1_4

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Clustering the Space1

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2009

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Cited by 7 publications

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“…We wish to mention that the approach of clustering the space into several objects or hyperspheres has been also implemented in the unsupervised learning/clustering framework see for example Ref. 22,41. Another approach, proposed in Ref.…”

Section: Clustering the Spacementioning

confidence: 99%

Hyperspherical Prototypes for Pattern Classification

Fayed

Atiya

Hashem

2009

Int. J. Patt. Recogn. Artif. Intell.

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The nearest neighbor method is one of the most widely used pattern classification methods. However its major drawback in practice is the curse of dimensionality. In this paper, we propose a new method to alleviate this problem significantly. In this method, we attempt to cover the training patterns of each class with a number of hyperspheres. The method attempts to design hyperspheres as compact as possible, and we pose this as a quadratic optimization problem. We performed several simulation experiments, and found that the proposed approach results in considerable speed-up over the k-nearest-neighbor method while maintaining the same level of accuray. It also significantly beats other prototype classification methods (Like LVQ, RCE and CCCD) in most performance aspects.

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Section: Clustering the Spacementioning

confidence: 99%