Learning sparse classifiers with difference of convex functions algorithms
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Publication:2867410
DOI10.1080/10556788.2011.652630zbMath1282.90181OpenAlexW2102984754MaRDI QIDQ2867410
Hoai An Le Thi, Cheng Soon Ong
Publication date: 19 December 2013
Published in: Optimization Methods and Software (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10556788.2011.652630
Related Items (19)
DC approximation approaches for sparse optimization ⋮ Sparse signal recovery via generalized Gaussian function ⋮ DC Programming and DCA for General DC Programs ⋮ Group variable selection via \(\ell_{p,0}\) regularization and application to optimal scoring ⋮ Feature selection for linear SVMs under uncertain data: robust optimization based on difference of convex functions algorithms ⋮ Feature selection in machine learning: an exact penalty approach using a difference of convex function algorithm ⋮ Sparse reconstruction via the mixture optimization model with iterative support estimate ⋮ Solution methodologies for minimizing a sum of pointwise minima of two functions ⋮ Neural network for a class of sparse optimization with \(L_0\)-regularization ⋮ A Method for Reducing the Number of Support Vectors in Fuzzy Support Vector Machine ⋮ Unified SVM algorithm based on LS-DC loss ⋮ Open issues and recent advances in DC programming and DCA ⋮ Sparse Covariance Matrix Estimation by DCA-Based Algorithms ⋮ A DC Programming Approach for Finding Communities in Networks ⋮ Minimizing a sum of clipped convex functions ⋮ DCA based algorithms for feature selection in multi-class support vector machine ⋮ DC programming and DCA: thirty years of developments ⋮ Minimization of transformed \(L_1\) penalty: theory, difference of convex function algorithm, and robust application in compressed sensing ⋮ DC Approximation Approach for ℓ0-minimization in Compressed Sensing
Cites Work
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- Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming
- 10.1162/153244303322753616
- Massive data discrimination via linear support vector machines
- Multicategory ψ-Learning
- Combined SVM-based feature selection and classification
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