Data-driven Calibration of Penalties for Least-Squares Regression

  title={Data-driven Calibration of Penalties for Least-Squares Regression},
  author={Sylvain Arlot and Pascal Massart},
  journal={Journal of Machine Learning Research},
Penalization procedures often suffer from their dependence on multiplying factors, whose optimal values are either unknown or hard to estimate from data. We propose a completely data-driven calibration algorithm for these parameters in the least-squares regression framework, without assuming a particular shape for the penalty. Our algorithm relies on the concept of minimal penalty, recently introduced by Birgé and Massart (2007) in the context of penalized least squares for Gaussian… CONTINUE READING
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