Generalized foley-sammon transform with kernels

Authors:
Zhenzhou Chen;Lei Li
Affiliations:
Institute of Software, Zhongshan University, Guangzhou, China;Institute of Software, Zhongshan University, Guangzhou, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

Citing 6
Cited 1

Kernel PCA and de-noising in feature spaces

Proceedings of the 1998 conference on Advances in neural information processing systems II
A generalized Foley-Sammon transform based on generalized fisher discriminant criterion and its application to face recognition

Pattern Recognition Letters
Kernel independent component analysis

The Journal of Machine Learning Research
Learning a kernel matrix for nonlinear dimensionality reduction

ICML '04 Proceedings of the twenty-first international conference on Machine learning
A fast iterative algorithm for fisher discriminant using heterogeneous kernels

ICML '04 Proceedings of the twenty-first international conference on Machine learning
An Optimal Set of Discriminant Vectors

IEEE Transactions on Computers

Kernel Generalized Foley-Sammon Transform with Cluster-Weighted

ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Fisher discriminant based Foley-Sammon Transform (FST) has great influence in the area of pattern recognition. On the basis of FST, the Generalized Foley-Sammon Transform (GFST) is presented. The main difference between the GFST and the FST is that the transformed sample set by GFST has the best discriminant ability in global sense while FST has this property only in part sense. Linear discriminants are not always optimal, so a new nonlinear feature extraction method GFST with Kernels (KGFST) based on kernel trick is proposed in this paper. Linear feature extraction in feature space corresponds to non-linear feature extraction in input space. Then, KGFST is proved to correspond to a generalized eigenvalue problem. Lastly, our method is applied to digits and images recognition problems, and the experimental results show that present method is superior to the existing methods in term of space distribution and correct classification rate.