Sparse high-dimensional fractional-norm support vector machine via DC programming

DOI10.1016/j.csda.2013.01.020zbMath1471.62080OpenAlexW1994484236MaRDI QIDQ1615094

Alexander Gray, Wei Guan

Publication date: 2 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.01.020

zbMATH Keywords

feature selectionsupport vector machinedifference of convex functions programmingfractional-norm SVMlow-sample-size high-dimensional data setreweighted \(L_1\)-norm SVM

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonconvex programming, global optimization (90C26) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

Solving norm constrained portfolio optimization via coordinate-wise descent algorithms ⋮ A new semi-supervised classifier based on maximum vector-angular margin ⋮ DC approximation approaches for sparse optimization ⋮ Feature selection in machine learning: an exact penalty approach using a difference of convex function algorithm ⋮ DC programming and DCA: thirty years of developments

Uses Software

• UCI-ml
• LIBSVM

Cites Work

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