Semi-supervised geometric mean of Kullback-Leibler divergences for subspace selection

@article{Chen2011SemisupervisedGM,
  title={Semi-supervised geometric mean of Kullback-Leibler divergences for subspace selection},
  author={Sibao Chen and Haixian Wang and Xingyi Zhang and Bin Luo},
  journal={2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)},
  year={2011},
  volume={2},
  pages={1232-1235}
}
Subspace selection is widely adopted in many areas of pattern recognition. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), is a successful method for subspace selection, which can significantly reduce the class separation problem. However, in many applications, labeled data are very limited… CONTINUE READING