ArticleJMLR workshop and conference proceedings2013
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems.
Article in JMLR workshop and conference proceedings, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
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- Geometric deep learning for diffusion MRI signal reconstruction with continuous samplings (DISCUS).Imaging neuroscience (Cambridge, Mass.) · 2024Article
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- Integrative Learning of Structured High-Dimensional Data from Multiple Datasets.Statistical analysis and data mining · 2023Article
- Neural Granger Causality.IEEE transactions on pattern analysis and machine intelligence · 2022Article
- Convex optimization algorithms in medical image reconstruction-in the age of AI.Physics in medicine and biology · 2022Review
- Feature selection for kernel methods in systems biology.NAR genomics and bioinformatics · 2022Article
- Poisson noisy image restoration via overlapping group sparse and nonconvex second-order total variation priors.PloS one · 2021Article
- WHERE DID THE TUMOR START? AN INVERSE SOLVER WITH SPARSE LOCALIZATION FOR TUMOR GROWTH MODELS.Inverse problems · 2020Article
- GRIA: Graphical Regularization for Integrative Analysis.Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining · 2020Article
- IMAGE-DRIVEN BIOPHYSICAL TUMOR GROWTH MODEL CALIBRATION.SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics · 2020Article
- Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering.Neuroinformatics · 2019Article
- A ROBUST AND EFFICIENT APPROACH TO CAUSAL INFERENCE BASED ON SPARSE SUFFICIENT DIMENSION REDUCTION.Annals of statistics · 2019Article
- Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction.IEEE transactions on medical imaging · 2019Article
- Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging.Frontiers in neuroscience · 2018Article
- Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization.IEEE transactions on medical imaging · 2017Article
- Multi-Tissue Decomposition of Diffusion MRI Signals via Sparse-Group Estimation.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2016Article
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5 authors.
Funding
Abstract
Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterparts in several sparse learning settings. However, solving the non-convex optimization problems associated with non-convex penalties remains a big challenge. A commonly used approach is the Multi-Stage (MS) convex relaxation (or DC programming), which relaxes the original non-convex problem to a sequence of convex problems. This approach is usually not very practical for large-scale problems because its computational cost is a multiple of solving a single convex problem. In this paper, we propose a General Iterative Shrinkage and Thresholding (GIST) algorithm to solve the nonconvex optimization problem for a large class of non-convex penalties. The GIST algorithm iteratively solves a proximal operator problem, which in turn has a closed-form solution for many commonly used penalties. At each outer iteration of the algorithm, we use a line search initialized by the Barzilai-Borwein (BB) rule that allows finding an appropriate step size quickly. The paper also presents a detailed convergence analysis of the GIST algorithm. The efficiency of the proposed algorithm is demonstrated by extensive experiments on large-scale data sets.
Identifiers
25285330PMC4184274What OpenQuestion holds
Registered trials
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