Deakin University
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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.



3369 - 3376


Guangzhou, China

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Publication classification

E1.1 Full written paper - refereed

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2005, IEEE

Title of proceedings

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