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Everything about Structured sparsity regularization
Structured sparsity regularization is a class of methods, and an area of research in statistical learning theory, that extend and generalize sparsity regularization learning methods. Both sparsity and structured sparsity regularization methods seek to exploit the assumption that the output variable to be learned can be described by a reduced number of variables in the input space. Sparsity regularization methods focus on selecting the input variables that best describe the output. Structured sparsity regularization methods generalize and extend sparsity regularization methods, by allowing for optimal selection over structures like groups or networks of input variables in. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Structured sparsity regularizationA 5-minute orientation
- Skim a recent paper: SeReNe: Sensitivity-Based Regularization of Neurons for Structured Sparsity in Neural NetworksInfluential work since 2022
- Read the landmark paper: Structured sparsity regularization for analyzing high-dimensional omics dataCited 63 times
- Go sideways: Proximal gradient methods for learningA closely related topic
- Go sideways: Matrix regularizationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Structured sparsity regularization for analyzing high-dimensional omics data
Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling
Nonlocal Structured Sparsity Regularization Modeling for Hyperspectral Image Denoising
Semi-Supervised Spectral Clustering With Structured Sparsity Regularization
Few‐view CT reconstruction with group‐sparsity regularization
Regularizers for structured sparsity
Weakly decomposable regularization penalties and structured sparsity
Deconvolution based photoacoustic reconstruction with sparsity regularization
🆕 Influential since 2022
SeReNe: Sensitivity-Based Regularization of Neurons for Structured Sparsity in Neural Networks
Feature flow regularization: Improving structured sparsity in deep neural networks
Non-line-of-sight reconstruction via structure sparsity regularization
One-step temporal subspace clustering with structured sparsity and indicator graph-Laplacian regularization
Dictionary Learning-Structured Reinforcement Learning With Adaptive-Sparsity Regularizer
📚 Books worth reading
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🔗 Related topics
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