A hybrid PCA-LDA model for dimension reduction
Nan Zhao, Washington Mio, Xiuwen Liu · 2011
Several variants of Linear Discriminant Analysis (LDA) have been investigated to address the vanishing of the within-class scatter under projection to a low-dimensional subspace in LDA. However, some of these proposals are ad hoc and some others do not address the problem of generalization to new data. Meanwhile, even though LDA is preferred in many application of dimension reduction, it does not always outperform Principal Component Analysis (PCA). In order to optimize discrimination performance in a more generative way, a hybrid dimension reduction model combining PCA and LDA is proposed in this paper. We also present a dimension reduction algorithm correspondingly and illustrate the method with several experiments. Our results have shown that the hybrid model outperform PCA, LDA and the combination of them in two separate stages.