A new approach to dimensionality reduction based on locality preserving LDA
Di Zhang, Jiazhong He · 2013
Linear discriminant analysis (LDA) is one of the most popular supervised dimensionality reduction (DR) techniques. However, LDA only captures global geometrical structure information of the data and ignores the geometrical variation of local data points of the same class. In this paper, a new supervised DR algorithm called local intraclass variation preserving LDA (LIPLDA) is proposed. We also show that the proposed algorithm can be extended to non-linear DR scenarios by applying the kernel trick.