Feature selection by independent component analysis and mutual information maximization in EEG signal classification

@article{Lan2005FeatureSB,
  title={Feature selection by independent component analysis and mutual information maximization in EEG signal classification},
  author={Tian Lan and Deniz Erdogmus and A. Adami and Michael Pavel},
  journal={Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.},
  year={2005},
  volume={5},
  pages={3011-3016 vol. 5}
}
Feature selection and dimensionality reduction are important steps in pattern recognition. In this paper, we propose a scheme for feature selection using linear independent component analysis and mutual information maximization method. The method is theoretically motivated by the fact that the classification error rate is related to the mutual information between the feature vectors and the class labels. The feasibility of the principle is illustrated on a synthetic dataset and its performance… CONTINUE READING
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