Sparse hierarchical regression with polynomials

Abstract We present a novel method for sparse polynomial regression. We are interested in that degree r polynomial which depends on at most k inputs, counting at most $$\ell$$ℓ monomial terms, and minimizes the sum of the squares of its prediction errors. Such highly structured sparse regression wa...

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Bibliographic Details
Main Authors: Bertsimas, Dimitris, Van Parys, Bart
Other Authors: Massachusetts Institute of Technology. Operations Research Center
Format: Article
Language:English
Published: Springer US 2021
Online Access:https://hdl.handle.net/1721.1/131533