A Path Algorithm for the Fused Lasso Signal Approximator - Hoefling, Holger (Journal of computational and graphical statistics, 2010)
The Lasso is a very well-known penalized regression model, which adds an L 1 penalty with parameter λ 1 on the coefficients to the squared error loss function. The Fused Lasso extends this model by also putting an L 1 penalty with parameter λ 2 on the difference of neighboring coefficients, assuming there is a natural ordering. In this article, we develop a path algorithm for solving the Fused Lasso Signal Approximator that computes the solutions for all values of λ 1 and λ 2 . We also present an approximate algorithm that has considerable speed advantages for a moderate trade-off in accuracy. In the Online Supplement for this article, we provide proofs and further details for the methods developed in the article.