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NPL
2006

A Fast Feature-based Dimension Reduction Algorithm for Kernel Classifiers

14 years 3 days ago
A Fast Feature-based Dimension Reduction Algorithm for Kernel Classifiers
This paper presents a novel dimension 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 principal component analysis (PCA), when the training data is complete, that is, representative of the ...
Senjian An, Wanquan Liu, Svetha Venkatesh, Ronny T
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where NPL
Authors Senjian An, Wanquan Liu, Svetha Venkatesh, Ronny Tjahyadi
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