Abstract
Linear subspace method based on principal component analysis has been applied for pattern recognition of high-dimensional data. However, the linear subspace method can not represent nonlinear characteristics of the data-distribution efficiently. In order to overcome this problem, some nonlinear subspace methods have been proposed. In this paper, we propose a novel nonlinear subspace method for pattern recognition using multi-layered perceptrons which can hierarchically construct a nonlinear subspace from the data-distribution. We introduce the optimization scheme of the subspace taking into account the classification. The proposed method achieves high classi-fication accuracy by the optimization scheme and provides the finite subspace to avoid the crossover of the subspaces. We examine its effectiveness through some experiments.
| Original language | English |
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| Title of host publication | Proceedings of the 8th IASTED International Conference on Signal and Image Processing, SIP 2006 |
| Pages | 45-51 |
| Number of pages | 7 |
| Publication status | Published - 2006 |
| Event | 8th IASTED International Conference on Signal and Image Processing, SIP 2006 and the 10th IASTED International Conference on Internet and Multimedia Systems and Applications, IMSA 2006 - Honolulu, HI Duration: 2006 Aug 14 → 2006 Aug 16 |
Other
| Other | 8th IASTED International Conference on Signal and Image Processing, SIP 2006 and the 10th IASTED International Conference on Internet and Multimedia Systems and Applications, IMSA 2006 |
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| City | Honolulu, HI |
| Period | 06/8/14 → 06/8/16 |
Keywords
- Eigenspace
- Feature extraction
- Neural networks
- Nonlinear PCA
- Pattern recognition
- Subspace method
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing