Name
Abstract | ||
|---|---|---|
The performance of a method for the reduction of the input space dimensionality of a physical or engineering problem is analyzed. The results of its application to several engineering problems are compared with those obtained by other well-known methods for the reduction of input space dimensionality, such as Principal Component Analysis and Independent Component Analysis. In order to carry out this study, the features extracted by the three methods were used as inputs to a feedforward neural network. The advantages of the proposed method are that it presents a computational complexity depending on the number of variables and guarantees dimensional homogeneity in the new space. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11550907_150 | ICANN (2) |
Keywords | Field | DocType |
feedforward neural network,well-known method,feature extraction,new space,independent component analysis,principal component analysis,engineering problem,input space dimensionality,guarantees dimensional homogeneity,computational complexity,modelling engineering problem,dimensional analysis | Dimensionality reduction,Computer science,Artificial intelligence,Diffusion map,Feedforward neural network,Pattern recognition,Algorithm,Curse of dimensionality,Feature extraction,Independent component analysis,Principal component analysis,Machine learning,Computational complexity theory | Conference |
Volume | ISSN | ISBN |
3697 | 0302-9743 | 3-540-28755-8 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noelia Sánchez-Maroño | 1 | 406 | 25.39 |
| Oscar Fontenla-Romero | 2 | 337 | 39.49 |
| Enrique Castillo | 3 | 555 | 59.86 |
| Amparo Alonso-Betanzos | 4 | 885 | 76.98 |