Research on Finsler metric in KNN algorithm

In order to overcome the shortcomings of traditional K nearest neighbor (ANN) algorithms in distance definition, this paper proposes a new KNN algorithm based on Finsler metric, FMKNN. The algorithm defines the distance between sample points as the Finsler metric and preserves the distance between s...

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Published inJisuanji Kexue yu Tansuo / Journal of Computer Science and Frontiers Vol. 5; no. 11; pp. 1021 - 1026
Main Authors Chen, Ming, He, Shuping, Li, Fanzhang
Format Journal Article
LanguageChinese
Published 01.11.2011
Subjects
Accuracy
Algorithms
Classification
Learning theory
Neural networks
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Online AccessGet full text
ISSN1673-9418
DOI10.3778/j.issn.1673-9418.2011.11.007

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Summary:In order to overcome the shortcomings of traditional K nearest neighbor (ANN) algorithms in distance definition, this paper proposes a new KNN algorithm based on Finsler metric, FMKNN. The algorithm defines the distance between sample points as the Finsler metric and preserves the distance between sample properties, making the distance between sample points more general. The experiment on handwritten data sets shows that, the classification accuracy of FMKNN algorithm is higher than traditional KNN algorithms.
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ISSN:1673-9418
DOI:10.3778/j.issn.1673-9418.2011.11.007