Abstract
In this paper, a novel Genetic Generalized Discriminant Analysis (GGDA) is proposed. GGDA is a generalized version of Exponential Discriminant Analysis (EDA). EDA algorithm is equivalent to map the samples to a new space and then perform LDA. However, is this space is optimal for classification? The proposed GGDA uses Genetic Algorithm to search for an more discriminant diffusing map and then perform LDA in the new space. The Experimental results confirm the efficiency of the proposed algorithm.
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Yan, L., Tang, L., Chu, SC., Zhu, X., Li, JB., Guo, X. (2014). Genetic Generalized Discriminant Analysis and Its Applications. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8481. Springer, Cham. https://doi.org/10.1007/978-3-319-07455-9_26
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DOI: https://doi.org/10.1007/978-3-319-07455-9_26
Publisher Name: Springer, Cham
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