Efficient Methods for Overlapping Group Lasso

By Lei Yuan, Jun Liu, Jieping Ye. 2011

235 citations recorded by the source corpus.

Subfields: Molecular Biology, Rheumatology, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Rheumatology

In Statistics and Probability

Citations in the corpus

Regularizing Text Categorization with Clusters of Words (2016, background): “Note that the groups can be overlapping (Jacob et al., 2009; Schmidt and Murphy, 2010; Jenatton et al., 2011; Yuan et al., 2011) even though it makes the optimization harder.”
SLEP: Sparse Learning with Efficient Projections (2011, methodology): “Based on our previous work [21, 25, 35, 36, 37, 38, 39, 40, 41, 42, 43, 58, 67, 78], we have developed the SLEP (Sparse Learning with Efficient Projections) package, which provides functions for solving a family of sparse learning algorithms.”
Linguistic Structured Sparsity in Text Categorization (2014, methodology): “There are many optimization methods for learning models with structured regularizers, particulary group lasso (Jacob et al., 2009; Jenatton et al., 2011; Chen et al., 2011; Qin and Goldfarb, 2012; Yuan et al., 2013).”
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]:”
Feature Selection (2016, background): “…in many real applications features could exhibit various kinds of structures, for example, spatial or temporal smoothness, disjoint groups, overlap groups, trees and graphs (Tibshirani et al. 2005; Jenatton et al. 2011; Yuan et al. 2011; Huang et al. 2011; Zhou et al. 2012; Wang and Ye 2015).”
Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers (2014, methodology): “To compute that, we employed the dual formulation proposed by Yuan et al. (2011).”
Pattern Recognition (2018, unknown): “Jain et al. [1] deﬁned soft biometrics to be the set of characteristics that provide some information for recognizing individuals, but that are not capable of distinguishing be-tween individuals, mainly due to the lack of distinctiveness and permanence.”
Multi-Task Feature Interaction Learning (2016, unknown): “And the tensor proximal: min can be solved in the same way as the group Lasso proximal operator [36].”
Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers (2014, background): “There has been much work on optimization with overlapping group lasso penalty (Jacob et al., 2009; Jenatton et al., 2011; Chen et al., 2011; Qin & Goldfarb, 2012; Yuan et al., 2013).”