Feature separability based on the distance matrix
Yu Zhu, Jinqiu Sun, Min Wang, Rui Yao, Yanning Zhang · 2017
Feature extraction is a key step in the classification and recognition problem. Features from different methods vary a lot with different separability in their feature space. We propose a novel method based on the distance matrix to evaluate feature separability by describing the in-class aggregation and the between-class scatter of every class. Finally the separability of each feature class is measured individually. Experiments on the synthetic data and ORL face dataset prove its effectiveness and advantage with regard to the conventional methods.