Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing

  title={Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing},
  author={Justin Ziniel and P. Schniter and Per Sederberg},
  journal={IEEE Transactions on Signal Processing},
  • Justin Ziniel, P. Schniter, Per Sederberg
  • Published 2014
  • Computer Science, Mathematics
  • IEEE Transactions on Signal Processing
  • For the problem of binary linear classification and feature selection, we propose algorithmic approaches to classifier design based on the generalized approximate message passing (GAMP) algorithm, recently proposed in the context of compressive sensing. We are particularly motivated by problems where the number of features greatly exceeds the number of training examples, but where only a few features suffice for accurate classification. We show that sum-product GAMP can be used to… CONTINUE READING
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