Sparsity and smoothness via the fused lasso

By Robert Tibshirani, Michael A. Saunders, Saharon Rosset, Ji Zhu, Keith Knight. 2005

2,988 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistics and Probability

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Citations in the corpus

Additive models with trend filtering (2017, background): “end ltering and locally adaptive regression spline estimates is exact in nite samples. It is also worth pointing out that when k= 0, the trend ltering estimate reduces to the 1d fused lasso estimate [Tibshirani et al., 2005], which is known as 1d total variation denoising in signal processing [Rudin”
Fused Lasso Additive Model (2014, background): “The addition of an 1 penalty on θ to (3) gives the fused lasso (Tibshirani et al. 2005).”
Optimization with Sparsity-Inducing Penalties (2011, background): “It can be found in the statistics literature under the name of “fused lasso” [135].”
Online Learning for Matrix Factorization and Sparse Coding (2009, positive): “For instance, it allows us to use the fast algorithm of Nesterov (2007) for solving the more general fused lasso problem (Tibshirani et al., 2005).”
Learning with Submodular Functions: A Convex Optimization Perspective (2011, neutral): “, [179, 37, 38]), machine learning and statistics [202].”
Learning Multi-Level Task Groups in Multi-Task Learning (2015, background): “It is worth mentioning that Ω(W) differs from the fused lasso regularizer (FLR) (Tibshirani et al. 2005) and its variant, generalized fused lasso regularizer (GFLR) (Friedman et al. 2007).”
An efficient algorithm for a class of fused lasso problems (2010, methodology): “Tibshirani et al. proposed the fused Lasso with the least squares loss [30].”
Structured sparsity through convex optimization (2011, background): “It can be found in the statistics literature under the name of “fused Lasso” (Tibshirani et al., 2005).”
Fast and Flexible ADMM Algorithms for Trend Filtering (2014, background): “When k = 0, we can see from the deﬁnition of D (1) in (2) that the trend ﬁltering problem (1) is the same as the 1-dimensional fused lasso problem (Tibshirani et al. 2005), also called 1-dimensional total variation denoising (Rudin et al. 1992), and hence the 0th order trend ﬁltering estimate ˆ β…”
Weakly decomposable regularization penalties and structured sparsity (2012, methodology): “Several types of modifications of the Lasso have been introduced and studied, such as the fused Lasso Tibshirani et al. (2005) and the smoothed Lasso Hebiri & van de Geer (2011).”
A New Primal–Dual Algorithm for Minimizing the Sum of Three Functions with a Linear Operator (2016, methodology): “Fused lasso [33] The fused lasso was proposed for group variable selection.”
Piecewise linear regularized solution paths (2007, methodology): “An example where a specific two-penalty formulation is natural and of great practical interest in a specific application can be found in our recent paper [17].”
Accelerated Attributed Network Embedding (2017, methodology): “The pioneer work of network lasso can also be traced back to the fused lasso [12, 37] and convex clustering [28] problems.”
Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso (2016, neutral): “…selection methods in detecting breaks in short panels; Baltagi et al. (2015) consider the estimation and identification of change points in large dimensional panel models with either stationary or nonstationary regressors and error terms; De Watcher and Tzavalis (2012) develop a testing…”