Variational Information Maximization for Feature Selection

@inproceedings{Gao2016VariationalIM,
  title={Variational Information Maximization for Feature Selection},
  author={Shuyang Gao and Greg Ver Steeg and Aram Galstyan},
  booktitle={NIPS},
  year={2016}
}
Feature selection is one of the most fundamental problems in machine learning. An extensive body of work on information-theoretic feature selection exists which is based on maximizing mutual information between subsets of features and class labels. Practical methods are forced to rely on approximations due to the difficulty of estimating mutual information. We demonstrate that approximations made by existing methods are based on unrealistic assumptions. We formulate a more flexible and general… CONTINUE READING
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