DocumentCode
2327098
Title
Unsupervised NN and graph matching approach to compare data sets
Author
Acciani, G. ; Fomarelli, G. ; Liturri, L.
Author_Institution
Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, Italy
Volume
4
fYear
2004
fDate
25-29 July 2004
Firstpage
2583
Abstract
We describe a technique to compare two data partitions of two different data sets as frequently occurs in defect detection. The comparison is obtained dividing each data set in partitions by means of an unsupervised neural network and associating an undirected complete weighted graph structure to these partitions. Then, a graph matching operation returns an estimation of the level of similarity between the data sets.
Keywords
image retrieval; neural nets; unsupervised learning; data sets comparison; defect detection; graph matching; undirected complete weighted graph structure; unsupervised neural network; Computational efficiency; Data analysis; Eigenvalues and eigenfunctions; Filters; Graph theory; Image retrieval; Instruments; Neural networks; Pattern recognition; Pixel;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-8359-1
Type
conf
DOI
10.1109/IJCNN.2004.1381053
Filename
1381053
Link To Document