Fast ramp fraction loss SVM classifier with low computational complexity for pattern classification

The support vector machine (SVM) is a powerful tool for pattern classification thanks to its outstanding efficiency. However, when encountering extensive classification tasks, the considerable computational complexity may present a substantial barrier. To reduce computational complexity, the novel r...

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Vydáno v:	Neural networks Ročník 184; s. 107087
Hlavní autoři:	Wang, Huajun, Li, Wenqian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.04.2025
Témata:	Algorithms Fast algorithm Humans Large-scale classification Low computational complexity Pattern Recognition, Automated - methods Ramp fraction loss SVM Sparsity and robustness Support Vector Machine Fast algorithm Sparsity and robustness Ramp fraction loss SVM Low computational complexity Large-scale classification
ISSN:	0893-6080, 1879-2782, 1879-2782
On-line přístup:	Získat plný text
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Popis
Shrnutí:	The support vector machine (SVM) is a powerful tool for pattern classification thanks to its outstanding efficiency. However, when encountering extensive classification tasks, the considerable computational complexity may present a substantial barrier. To reduce computational complexity, the novel ramp fraction loss SVM model called Lrf-SVM is introduced. The aim of this model is to simultaneously achieve sparsity and robustness. Utilizing our proposed proximal stationary point, we develop a novel optimality theory to the nonsmooth and nonconvex Lrf-SVM. Drawing upon this innovative theory, a novel efficient alternating direction method of multipliers (ADMM) incorporating a working set with low computational complexity will be introduced for handing Lrf-SVM. Moreover, our algorithm has shown that it can achieve global convergence. Our algorithm has been shown to be highly efficient through numerical experiments, surpassing nine other top solvers regarding number of support vectors, speed of computation, accuracy of classification and robust to outliers. For example, for addressing the real dataset over 107 samples, our algorithm can finish the classification in just 18.67 s, a notable enhancement in comparison to other algorithms that need at least 605.3 s. •We construct a novel ramp fraction loss function.•We propose a novel robust and sparse ramp fraction loss SVM optimization model.•We propose a new and effective ADMM with working set to solve the ramp fraction loss SVM.•We compare our algorithm with other nine leading solvers. Numerical experiments demonstrate that our algorithm shares excellent performance.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0893-6080 1879-2782 1879-2782
DOI:	10.1016/j.neunet.2024.107087