In the framework of supervised learning we prove that the iterative algorithm introduced in Umanità and Villa (2010) allows to estimate in a consistent way the relevant features of the regression function under the a priori assumption that it admits a sparse representation.
A consistent algorithm to solve Lasso, elastic-net and Tikhonov regularization.
DE VITO, ERNESTO;VILLA, SILVIA;UMANITA', VERONICA
2011-01-01
Abstract
In the framework of supervised learning we prove that the iterative algorithm introduced in Umanità and Villa (2010) allows to estimate in a consistent way the relevant features of the regression function under the a priori assumption that it admits a sparse representation.
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