Feature selection based on loss-margin of nearest neighbor classification
The problem of selecting a subset of relevant features is classic and found in many branches of science includingexamples in pattern recognition. In this paper, we propose a new feature selection criterion based on low-loss nearest neighbor classification and a novel feature selection algorithm that optimizes the margin of nearest neighbor classification through minimizing its loss function. At the same time, theoretical analysis based on energy-based model is presented, and some experiments are also conducted on several benchmark real-world data sets and facial data sets for gender classification to show that the proposed feature selection method outperforms other classic ones.
feature selection margin loss function energy-based model