Sparse Volterra and Polynomial Regression Models: Recoverability and Estimation

@article{Kekatos2011SparseVA,
  title={Sparse Volterra and Polynomial Regression Models: Recoverability and Estimation},
  author={Vassilis Kekatos and Georgios B. Giannakis},
  journal={IEEE Transactions on Signal Processing},
  year={2011},
  volume={59},
  pages={5907-5920}
}
Volterra and polynomial regression models play a major role in nonlinear system identification and inference tasks. Exciting applications ranging from neuroscience to genome-wide association analysis build on these models with the additional requirement of parsimony. This requirement has high interpretative value, but unfortunately cannot be met by least-squares based or kernel regression methods. To this end, compressed sampling (CS) approaches, already successful in linear regression settings… CONTINUE READING
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