Recursive Lasso: A Homotopy Algorithm for Lasso with Online Observations
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Authors: P. Garrigues, L. El Ghaoui.
Status: In Proc. NIPS, 2008.
Abstract: It has been shown that the problem of
-penalized least-squares regression, commonly referred to as the Lasso or Basis Pursuit De-Noising leads to solutions that are sparse and therefore achieves model selection. We propose in this paper an
algorithm to solve the Lasso with online (sequential) observations. We introduce
an optimization problem that allows us to compute an homotopy from the current
solution to the solution after observing a new data point. We compare our method
to Lars and present an application to compressed sensing with sequential observations. Our approach can also be easily extended to compute an homotopy from the
current solution to the solution that corresponds to removing a data point, which
leads to an efficient algorithm for leave-one-out cross-validation.
Code: .zip.
Bibtex reference:
@conference{GaE:08,
Author = {P. Garrigues and L. {El Ghaoui}},
Booktitle = {Proc. NIPS},
Title = {An Homotopy Algorithm for the Lasso with Online Observations},
Year = {2008}}