Structural Pattern Recognition with Graph Edit Distance

2015 ◽  
Author(s):  
Kaspar Riesen
Keyword(s):  
Pattern Recognition ◽  
Edit Distance ◽  
Graph Edit Distance ◽  
Structural Pattern ◽  
Structural Pattern Recognition

scholarly journals Redefining the Graph Edit Distance

2021 ◽  
Vol 2 (6) ◽  
Author(s):  
Francesc Serratosa
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Triangle Inequality ◽  
Edit Distance ◽  
Minimum Amount ◽  
Graph Edit Distance ◽  
Edit Operation ◽  
Attributed Graphs ◽  
Distance Properties

AbstractGraph edit distance has been used since 1983 to compare objects in machine learning when these objects are represented by attributed graphs instead of vectors. In these cases, the graph edit distance is usually applied to deduce a distance between attributed graphs. This distance is defined as the minimum amount of edit operations (deletion, insertion and substitution of nodes and edges) needed to transform a graph into another. Since now, it has been stated that the distance properties have to be applied [(1) non-negativity (2) symmetry (3) identity and (4) triangle inequality] to the involved edit operations in the process of computing the graph edit distance to make the graph edit distance a metric. In this paper, we show that there is no need to impose the triangle inequality in each edit operation. This is an important finding since in pattern recognition applications, the classification ratio usually maximizes in the edit operation combinations (deletion, insertion and substitution of nodes and edges) that the triangle inequality is not fulfilled.

Structural Pattern Recognition

2001 ◽  
pp. 243-289 ◽  
Author(s):  
Joaquim P. Marques de Sá
Keyword(s):  
Pattern Recognition ◽  
Structural Pattern ◽  
Structural Pattern Recognition
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