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
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;
Conference_Titel :
Neural Networks, 1996., IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-3210-5
DOI :
10.1109/ICNN.1996.548893
