Jason C. Isaacs
ABSTRACT
Kernel methods are mathematical tools that provide higher dimensional representation of given data set in feature space for pattern recognition and data analysis problems. Optimal Component Analysis (OCA) [4] poses the problem of finding an optimal linear representation. In this paper we present the results of six kernel functions and their respective performance for Evolutionary Computation-based kernel OCA on the Pima Indian Diabetes database. Empirical results show that we outperform existing techniques on this database.
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- Q. Zhang and X. Liu, "Kernel Optimal Component Analysis," proceedings of the 2004 IEEE Computer Vision and Pattern Recognition Workshops. Google ScholarDigital Library
Index Terms
- Evolutionary computation-based kernel optimal component analysis for pattern recognition
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