Geometric linear discriminant analysis for pattern recognition

摘要

We propose an innovative technique, geometric linear discriminant analysis (Geometric LDA), to reduce the complexity of pattern recognition systems by using a linear transformation to lower the dimension of the observation space. We experimentally compare Geometric LDA to other dimensionality reduction methods found in the literature. We show that Geometric LDA produces the same and in many cases a significantly better linear transformation than other methods found in the literature.