Stationary point variational Bayesian attribute-distributed sparse learning with ℓ1 sparsity constraints

@article{Shutin2011StationaryPV,
  title={Stationary point variational Bayesian attribute-distributed sparse learning with ℓ1 sparsity constraints},
  author={Dmitriy Shutin and Sanjeev R. Kulkarni and H. Vincent Poor},
  journal={2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
  year={2011},
  pages={277-280}
}
The paper proposes a new variational Bayesian algorithm for ℓ1-penalized multivariate regression with attribute-distributed data. The algorithm is based on the variational Bayesian version of the SAGE algorithm that realizes a training of individual agents in a distributed fashion and sparse Bayesian learning (SBL) with hierarchical sparsity prior modeling of the agent weights. The SBL introduces constraints on the weights of individual agents, thus reducing the effects of overfitting and… CONTINUE READING