Multi-variable estimation-based safe screening rule for small sphere and large margin support vector machine

Multi-variable estimation-based safe screening rule for small sphere and large margin support vector machine
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基于多变量估计的小球大边缘支持向量机安全筛选规则

DOI:
10.1016/j.knosys.2019.105223
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发表时间:
2020-03
影响因子:
8.8
通讯作者:
Du Junling
Du Junling
中科院分区:
计算机科学1区
文献类型:
--
作者:
Cao Yuzhou;Xu Yitian;Du Junling

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摘要小球面大间隔支持向量机(SSLM)是新奇检测领域最具竞争力的方法之一。然而,现有的SSLM求解器不能处理大数据,由于昂贵的时间成本。虽然最近出现的安全筛选方法可以有效地提高计算速度,但它不适用于SSLM,因为SSLM具有多个变量,这些变量不能用训练样本的线性组合显式表示。在本文中,我们通过整合ν-p r r r t y,KKT条件和变分不等式,构造了一个新的SSLM安全筛选规则(MVE-SSR-SSLM).这是一族多变量超球支持向量机的第一个安全筛选规则。在实际解决问题之前移除非活性样品,以加速解决过程而不损失任何安全性。在15个基准数据集和中国葡萄酒数据集上的数值实验表明了该方法的有效性和稳定性。
Abstract Small Sphere and Large Margin (SSLM) SVM is one of the most competitive methods for Novelty Detection. However, the existing solvers for SSLM cannot deal with large data due to the expensive time cost. Although recently emerged safe screening methods can effectively enhance the computational speed, it is not available for SSLM because SSLM has multiple variables which cannot be represented explicitly by the linear combination of training samples. In this work, we construct a new safe screening rule for SSLM (MVE-SSR-SSLM) by integrating the ν-p r o p e r t y, KKT conditions and variational inequalities. It is the first safe screening rule for a family of hypersphere support vector machine with multiple variables. The inactive samples are removed before actually solving the problem to accelerate the solving procedure without any loss of safety. Numerical experiments on fifteen benchmark datasets and Chinese wine dataset are conducted to show the validity and stability of the proposed MVE-SSR-SSLM.
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