抄録
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.
| 本文言語 | English |
|---|---|
| ホスト出版物のタイトル | Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence |
| 編集者 | M.H. Hamza |
| ページ | 66-72 |
| ページ数 | 7 |
| 出版ステータス | Published - 2004 |
| イベント | Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence - Grindelwald 継続期間: 2004 2月 23 → 2004 2月 25 |
Other
| Other | Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence |
|---|---|
| City | Grindelwald |
| Period | 04/2/23 → 04/2/25 |
ASJC Scopus subject areas
- 工学（全般）