Non-convex feature learning via l p, ∞ operator

Keywords

Norm (philosophy), Proximal gradient methods for learning, Regularization (linguistics), Regular polygon, Feature selection

References

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Cited by

• Exclusive Feature Learning on Arbitrary Structures via ℓ_1, 2-normDeguang Kong, Ryohei Fujimaki, Ji Liu — 2014
• Spectrally constrained L1-norm improves quantitative accuracy of diffuse optical tomographyWenqi Lu, Iain B. Styles — 2017
• L1-norm based nonlinear reconstruction improves quantitative accuracy of spectral diffuse optical tomographyWenqi Lu, Daniel Lighter, Iain B. Styles — 2018
• Cost-Sensitive Feature Selection by Optimizing F-MeasuresMeng Liu, Chang Xu, Yong Luo — 2018
• Discriminative Feature Selection via Employing Smooth and Robust Hinge LossHanyang Peng, Cheng‐Lin Liu — 2019
• Commercial Video Evaluation via Low-Level Feature Extraction and SelectionXiangmin Lun, Mingxuan Wang, Zhenglin Yu — 2018
• University of Birmingham L1-norm Based Nonlinear Reconstruction Improves Quantitative Accuracy of Spectral Diffuse Optical TomographyLu Wenqi, D. Lighter, I. Styles — 2018
• University of Birmingham L1-norm Based Nonlinear Reconstruction Improves Quantitative Accuracy of Spectral Diffuse Optical TomographyAin, S. B., Tyles — 2018

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