DocumentCode
303219
Title
Multivariate data projection techniques based on a network of enhanced neural elements
Author
Acciani, G. ; Chiarantoni, E. ; Minenna, M. ; Vacca, F.
Author_Institution
Dipartimento di Elettrotecnica ed Elettronica, Bari Univ., Italy
Volume
1
fYear
1996
fDate
3-6 Jun 1996
Firstpage
211
Abstract
In this paper two techniques to project high dimensional data into a bidimensional space are introduced. These techniques are based on an unsupervised neural network of enhanced processing elements. The proposed approaches are compared with some widely known projection techniques based on unsupervised neural networks. These comparisons show that the new projection techniques perform comparably or slightly better than the traditional techniques and are promising in term of computational burden
Keywords
data analysis; self-organising feature maps; unsupervised learning; bidimensional space; data analysis; enhanced neural elements; high dimensional data; multivariate data projection; self tuning neural nets; unsupervised neural network; Artificial neural networks; Biological cells; Biological system modeling; Data analysis; Neural networks; Neurons; Signal analysis; Space exploration; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1996., IEEE International Conference on
Conference_Location
Washington, DC
Print_ISBN
0-7803-3210-5
Type
conf
DOI
10.1109/ICNN.1996.548893
Filename
548893
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