Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction

@inproceedings{Lozano2009GroupedOM,
  title={Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction},
  author={Aurelie C. Lozano and Grzegorz Swirszcz and Naoki Abe},
  booktitle={NIPS},
  year={2009}
}
We consider the problem of variable group selection for least squares regression, namely, that of selecting groups of variables for best regression performance, leveraging and adhering to a natural grouping structure within the explanatory variables. We show that this problem can be efficiently addressed by using a certain greedy style algorithm. More precisely, we propose the Group Orthogonal Matching Pursuit algorithm (Group-OMP), which extends the standard OMP procedure (also referred to as… CONTINUE READING

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