Papers › A path algorithm for the Fused Lasso Signal Approximator
A path algorithm for the Fused Lasso Signal Approximator
Holger Hoefling
The Lasso is a very well known penalized regression model, which adds an L₁ penalty with parameter λ₁ on the coefficients to the squared error loss function. The Fused Lasso extends this model by also putting an L₁ penalty with parameter λ₂ on the difference of neighboring coefficients, assuming there is a natural ordering. In this paper, we develop a fast path algorithm for solving the Fused Lasso Signal Approximator that computes the solutions for all values of λ₁ and λ₂. In the supplement, we also give an algorithm for the general Fused Lasso for the case with predictor matrix ∈R^(n ×p) with rank()=p.
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