venkatesh-afastkernel-2005.pdf (4.81 MB)
A fast kernel dimension reduction algorithm with applications to face recognition
conference contribution
posted on 2005-01-01, 00:00 authored by S An, W Liu, Svetha VenkateshSvetha Venkatesh, R TjahyadiThis 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
Event
International Conference on Machine Learning and Cybernetics (4th : 2005 : Guangzhou, China)Pagination
3369 - 3376Publisher
IEEELocation
Guangzhou, ChinaPlace of publication
[Washington, D. C.]Publisher DOI
Start date
2005-08-18End date
2005-08-21ISBN-10
0780390911Language
engNotes
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Publication classification
E1.1 Full written paper - refereedCopyright notice
2005, IEEETitle of proceedings
ICMLC 2005 : Proceedings of the 4th International Conference on Machine Learning and CyberneticsUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC