Abstract
Principal Component Analysis (PCA) is a useful method in multivariate analysis to reduce the dimensionality of data. We have already proposed a non-linearly extended model of PCA by employing neural networks and have shown its effectiveness with some artificial data. In this paper, we report results of a nonlinear principal component analysis on real-world data utilizing the proposed method. Moreover, we compare the distribution of reconstructed data with the distribution of the original data to discuss the advantage of nonlinear PCA.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence |
| Editors | M.H. Hamza |
| Pages | 66-72 |
| Number of pages | 7 |
| Publication status | Published - 2004 |
| Event | Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence - Grindelwald Duration: 2004 Feb 23 → 2004 Feb 25 |
Other
| Other | Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence |
|---|---|
| City | Grindelwald |
| Period | 04/2/23 → 04/2/25 |
Keywords
- Dimensionality reduction
- MLP
- Nonlinear PCA
ASJC Scopus subject areas
- Engineering(all)