An efficient algorithm for a class of fused lasso problems

By Jun Liu, Lei Yuan, Jieping Ye. 2010

216 citations recorded by the source corpus.

Subfields: Computational Mechanics, Epidemiology, Statistics and Probability

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Citations in the corpus

Fused Lasso Additive Model (2014, methodology): “Several algorithms for solving (3) have been proposed [Hoeﬂing, 2010, Johnson, 2013, Liu et al., 2010].”
Modeling disease progression via multi-task learning (2013, unknown): “(21) can be effectively solved using (Liu et al., 2010).”
Integrating low-rank and group-sparse structures for robust multi-task learning (2011, background): “APM has attracted extensive attentions in the machine learning and data mining communities [20, 19, 15, 8, 18, 21] due to its optimal convergence rate among all first-order techniques and its ability of dealing with large-scale non-smooth optimization problems.”
Adaptive Restart for Accelerated Gradient Schemes (2012, background): “For strongly convex functions an alternative to choosing the optimal value of q in Algorithm 1 is to use restarting, [3, 11, 15, 16, 20].”
Robust multi-task feature learning (2012, methodology): “Here we propose to employ the accelerated gradient descent [25, 26] to solve the optimization problem, which generates the solution at the k-th iteration (k ≥ 1) by computing the following proximal operator [20, 19, 21, 12, 37, 3]:”
SLEP: Sparse Learning with Efficient Projections (2011, background): “The functions described in the following subsections are based on our work in [39].”
Seismic sparse-spike deconvolution via Toeplitz-sparse matrix factorization (2016, methodology): “Then, substituting equation 20 into equation 13 gives the following Fused Lasso problem: Jα ¼ minα 1 2 k ~y − ~Rαk22 þ βkαk1 þ β1 X k jakþ1 − akj; (21) which can be solved by any of the standard methods (e.g., Hoefling, 2010; Liu et al., 2010; Ye and Xie, 2011).”
A Direct Algorithm for 1-D Total Variation Denoising (2013, methodology): “Evidence of this is that iterative methods are regularly proposed for 1-D TV denoising [32]–[34].”