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
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;
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1381053
