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
复制标题
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
中科院分区:
文献类型:
--
作者:
Cao Yuzhou;Xu Yitian;Du Junling
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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影响因子:
2.9
作者:
Vapnik, V;Chapelle, O
通讯作者:
Chapelle, O
DOI:
10.1016/j.knosys.2008.03.044
发表时间:
2008-12
期刊:
Knowl. Based Syst.
影响因子:
--
作者:
Wen Zhang;Taketoshi Yoshida;Xijin J. Tang
通讯作者:
Wen Zhang;Taketoshi Yoshida;Xijin J. Tang
影响因子:
8.8
作者:
Wang Huiru;Zhou Zhijian
通讯作者:
Zhou Zhijian
DOI:
10.1090/s0002-9947-1950-0051437-7
发表时间:
1950-01-01
影响因子:
1.3
作者:
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通讯作者:
ARONSZAJN, N
影响因子:
7.5
作者:
Tax, DMJ;Duin, RPW
通讯作者:
Duin, RPW