A path algorithm for the Fused Lasso Signal Approximator

2009/10/03 by Hölger Hoefling, Hoefling, Holger · 4 citations
Engineering · Mathematics · #Computation (stat.CO) #Control Systems and Identification #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (stat.ML) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.0910.0526

openalex publication_date 2009/10/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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 \bX ∈ \mathdsR^{n × p} with rank(\bX)=p.

Citations

• Least angle regression

Cited by

• Asymptotic Distribution of Constrained Nearly-Isotonic Graph Fused Lasso
• Developing an information criterion for spatial data analysis through Bayesian generalized fused lasso
• Shape-Informed Clustering of Multi-Dimensional Functional Data via Deep Functional Autoencoders
• Spacing Test for Fused Lasso

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