Name
Title | ||
|---|---|---|
Use Of Multilayer Feedforward Neural Nets As A Display Method For Multidimensional Distributions |
Abstract | ||
|---|---|---|
We present a new method based on multilayer feedforward neural nets for displaying an n-dimensional distribution in a projected space of 1, 2 or 3 dimensions. A fully nonlinear net with several hidden layers is used. Efficient learning is achieved using multi-seed backpropagation. As a principal component analysis (PCA), the proposed method is useful for extracting information on the structure of the data set, but unlike the PCA, the transformation between the original distribution and the projected one is not restricted to be linear. Artificial examples and a real application are presented in order to show the reliability and potential of the method. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1142/S0129065795000202 | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | Field | DocType |
neural net | Nonlinear system,Pattern recognition,Computer science,Artificial intelligence,Artificial neural network,Backpropagation,Principal component analysis,Machine learning,Feed forward | Journal |
Volume | Issue | ISSN |
6 | 3 | 0129-0657 |
Citations | PageRank | References |
2 | 0.48 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lluís Garrido | 1 | 17 | 4.11 |
| Vicens Gaitan | 2 | 4 | 1.93 |
| S Gómez | 3 | 6 | 2.03 |
| Xavier Calbet | 4 | 113 | 19.95 |