Efficient and robust feature extraction by maximum margin criterion

@article{Li2003EfficientAR,
  title={Efficient and robust feature extraction by maximum margin criterion},
  author={Haifeng Li and Tao Jiang and Keshu Zhang},
  journal={IEEE Transactions on Neural Networks},
  year={2003},
  volume={17},
  pages={157-165}
}
In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the two most popular linear dimensionality reduction methods. However, PCA is not very effective for the extraction of the most discriminant features, and LDA is not stable due to the small sample size problem . In this paper, we propose some new (linear and… CONTINUE READING