The group fused Lasso for multiple change-point detection

2011/06/21 by Kevin Bleakley, Bleakley, Kevin, Jean-Philippe Vert +2 · 2 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · #Advanced Statistical Methods and Models #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (stat.ML) #Quantitative Methods (q-bio.QM) #Statistical Methods and Inference #q-bio.QM #stat.ML

paper · pdf · doi:10.48550/arxiv.1106.4199

arxiv created 2011/06/21 · openalex publication_date 2011/06/21 · arxiv updated 2011/06/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present the group fused Lasso for detection of multiple change-points shared by a set of co-occurring one-dimensional signals. Change-points are detected by approximating the original signals with a constraint on the multidimensional total variation, leading to piecewise-constant approximations. Fast algorithms are proposed to solve the resulting optimization problems, either exactly or approximately. Conditions are given for consistency of both algorithms as the number of signals increases, and empirical evidence is provided to support the results on simulated and array comparative genomic hybridization data.

Citations

• Penalized Regressions: The Bridge versus the Lasso
• Nonlinear total variation based noise removal algorithms

Cited by

• Image edges resolved well when using an overcomplete\n piecewise-polynomial model
• Bayesian Model for Multiple Change-points Detection in Multivariate Time\n Series

Related

• Element-wise estimation error of a total variation regularized estimator for change point detection
• Approximate Recovery in Changepoint Problems, from ℓ₂ Estimation Error Rates
• Fast Overlapping Group Lasso
• On change point detection using the fused lasso method
• Bayesian Model for Multiple Change-points Detection in Multivariate Time\n Series