The solution path of the generalized lasso

Abstract

We present a path algorithm for the generalized lasso problem. This problem penalizes the ${\ell }_1$ norm of a matrix D times the coefficient vector, and has a wide range of applications, dictated by the choice of D. Our algorithm is based on solving the dual of the generalized lasso, which greatly facilitates computation of the path. For $D=I$ (the usual lasso), we draw a connection between our approach and the well-known LARS algorithm. For an arbitrary D, we derive an unbiased estimate of the degrees of freedom of the generalized lasso fit. This estimate turns out to be quite intuitive in many applications.

Published in at http://dx.doi.org/10.1214/11-AOS878 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (3)

• Pathwise coordinate optimization
• On the "degrees of freedom" of the lasso
• Piecewise linear regularized solution paths

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