Kernel Fisher Discriminant Analysis with Locality Preserving for Feature Extraction and Recognition
Many previous studies have shown that class classification can be greatly improved by kernel Fisher discriminant analysis (KDA) technique. However, KDA only captures global geometrical structure and disregards local geometrical structure of the data. In this paper, we propose a new feature extractio...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Atlantis Press
2013-12-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25868440.pdf |