Iterative Reweighted 1 and 2 Methods for Finding Sparse Solutions

By David Paul Wipf, Srikantan S. Nagarajan. IEEE Journal on Selected Topics in Signal Processing, 2010

466 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3d3919f11a515969252db307988a5deff17be7e1

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In Biomedical Engineering

In Computational Mechanics

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Citations in the corpus

Latent Variable Bayesian Models for Promoting Sparsity (2011, background): “For example, it seems plausible (desirable) that if and all columns of are not close to , then possibly (hopefully) we are not at a local minimum and a sparser solution can be descended upon by including .”
Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework (2014, methodology): “…most MTL algorithms is that all tasks are related and so a block-regularization with 1 // q norm (q > 1) is frequently used in the MTL literatures (Wipf and Nagarajan 2010; Obozinski, Taskar, and Jordan 2006; Xiong et al. 2006; Argyriou, Evgeniou, and Pontil 2008; Liu, Ji, and Ye 2009; Quattoni…”
Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning (2011, methodology): “On the other hand, due to the works in [25], [27], [54], which connected SBL algorithms to traditional convex relax ation methods such as Lasso [37] and Basis Pursuit Denoising [38], it was found thatλ is functionally the same as the regularization parameters of those convex relaxation algorit hms.”
Which Looks Like Which: Exploring Inter-class Relationships in Fine-Grained Visual Categorization (2014, methodology): “Another way to optimize the mixture norm penalty is to iteratively solve a series of reweighted 2 problems [38].”
Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection (2017, methodology): “In this section, we design the iteratively reweighed framework [59] to optimize the objective function in (6) via iteratively calculating the gradient of ‖S‖2,1.”
Probabilistic Multi-Task Feature Selection (2010, methodology): “Based on this probabilistic interpretation, we then develop a probabilistic model which, among other things, can solve the model selection problem automatically by estimating q from data.”