Abstract
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×n metrics. Another problem is the complexity for the eigenvalue problem of n×n metrics is O(n 3). 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.
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Chen, Z. (2007). Kernel Generalized Foley-Sammon Transform with Cluster-Weighted. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_94
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DOI: https://doi.org/10.1007/978-3-540-74205-0_94
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