Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction

NeurIPS 2009 Grzegorz SwirszczNaoki AbeAurelie C. Lozano

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... (read more)

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