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.
['Lijun Yan', 'Lin-Lin Tang', 'Shu-Chuan Chu', 'Xiaorui Zhu', 'Jun-Bao Li', 'Xiaochuan Guo']
Genetic Generalized Discriminant Analysis and Its Applications