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Fast generalized ramp loss support vector machine for pattern classification
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DOI：10.1016/j.patcog.2023.109987.png)
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Abstract
En 中文
Support vector machine (SVM) is widely recognized as an effective classification tool and has demonstrated superior performance in diverse applications. However, for large-scale pattern classification problems, it may require much memory and incur prohibitive computational costs. Motivated by this, we propose a new SVM model with novel generalized ramp loss (LR-SVM). The first-order optimality conditions for the non-convex and non-smooth LR-SVM are developed by the newly developed P-stationary point, based on which, the LR support vectors and working set of LR-SVM are defined, interestingly, which shows that all of the LR support vectors are on the two support hyperplanes under mild conditions. A fast proximal alternating direction method of multipliers with working set (LR-ADMM) is developed to handle LR-SVM and LR-ADMM has been demonstrated to achieve global convergence while maintaining a significantly low computational complexity. Numerical comparisons with nine leading solvers show that LR-ADMM demonstrates outstanding performance, particularly when applied to large-scale pattern classification problems with fewer support vectors, higher prediction accuracy and shorter computational time.
Keywords:
Generalized ramp loss
LR-SVM
LR proximal operator
Working set
LR support vectors
LR-ADMM
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
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