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IEEE Transactions on Pattern Analysis and Machine Intelligence
Article . 2007 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
DBLP
Article . 2018
Data sources: DBLP
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A Normalized Levenshtein Distance Metric

Authors: Yujian Li; Bi Liu;

A Normalized Levenshtein Distance Metric

Abstract

Although a number of normalized edit distances presented so far may offer good performance in some applications, none of them can be regarded as a genuine metric between strings because they do not satisfy the triangle inequality. Given two strings X and Y over a finite alphabet, this paper defines a new normalized edit distance between X and Y as a simple function of their lengths (|X| and |Y|) and the Generalized Levenshtein Distance (GLD) between them. The new distance can be easily computed through GLD with a complexity of O(|X|.|Y|) and it is a metric valued in [0, 1] under the condition that the weight function is a metric over the set of elementary edit operations with all costs of insertions/deletions having the same weight. Experiments using the AESA algorithm in handwritten digit recognition show that the new distance can generally provide similar results to some other normalized edit distances and may perform slightly better if the triangle inequality is violated in a particular data set.

Related Organizations
Keywords

Imaging, Three-Dimensional, Models, Statistical, Artificial Intelligence, Image Interpretation, Computer-Assisted, Information Storage and Retrieval, Image Enhancement, Algorithms, Pattern Recognition, Automated

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    549
    popularity
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    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
549
Top 0.1%
Top 0.1%
Top 10%
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