An optimal algorithm for approximate nearest neighbor searching
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
An Optimal Set of Discriminant Vectors
IEEE Transactions on Computers
Generalized foley-sammon transform with kernels
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
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KGFST (Kernel Generalized Foley-Sammon Transform) has been proved very successfully in the area of pattern recognition. By the kernel trick, one can calculate KGFST in input space instead of feature space to avoid high dimensional problems. But one has to face two problems. In many applications, when n(the number of samples) is very large, it not realistic to store and calculate serval n×nmetrics. Another problem is the complexity for the eigenvalue problem of n×nmetrics is O(n3). So a new nonlinear feature extraction method CW-KGFST (KGFST with Cluster-weighted) based on KGFST and Clustering is proposed in this paper. Through Cluster-weighted, the number of samples can be reduced, the calculate speed can be higher and the accuracy can be preserved simultaneously. Lastly, our method is applied to digits and images recognition problems, and the experimental results show that the performance of present method is superior to the original method.