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A fast kernel dimension reduction algorithm with applications to face recognition

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conference contribution
posted on 2005-01-01, 00:00 authored by S An, W Liu, Svetha VenkateshSvetha Venkatesh, R Tjahyadi
This paper presents a novel dimensionality reduction algorithm for kernel based classification. In the feature space, the proposed algorithm maximizes the ratio of the squared between-class distance and the sum of the within-class variances of the training samples for a given reduced dimension. This algorithm has lower complexity than the recently reported kernel dimension reduction(KDR) for supervised learning. We conducted several simulations with large training datasets, which demonstrate that the proposed algorithm has similar performance or is marginally better compared with KDR whilst having the advantage of computational efficiency. Further, we applied the proposed dimension reduction algorithm to face recognition in which the number of training samples is very small. This proposed face recognition approach based on the new algorithm outperforms the eigenface approach based on the principle component analysis (PCA), when the training data is complete, that is, representative of the whole dataset.

History

Pagination

3369 - 3376

Location

Guangzhou, China

Open access

  • Yes

Start date

2005-08-18

End date

2005-08-21

ISBN-10

0780390911

Language

eng

Notes

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Publication classification

E1.1 Full written paper - refereed

Copyright notice

2005, IEEE

Title of proceedings

ICMLC 2005 : Proceedings of the 4th International Conference on Machine Learning and Cybernetics