ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (9): 1979-1991.doi: 10.7544/issn1000-1239.2017.20160519

• 人工智能 • 上一篇    下一篇

面向大规模数据属性效应控制的核心向量回归机

刘解放1,2,王士同1,王骏1,邓赵红1   

  1. 1(江南大学数字媒体学院 江苏无锡 214122);2(湖北交通职业技术学院交通信息学院 武汉 430079) (ljf-it@163.com)
  • 出版日期: 2017-09-01
  • 基金资助: 
    国家自然科学基金项目(61300151,61572236);江苏省杰出青年基金项目(BK20140001);江苏省自然科学基金项目(BK20130155,BK20151299)

Core Vector Regression for Attribute Effect Control on Large Scale Dataset

Liu Jiefang1,2, Wang Shitong1, Wang Jun1, Deng Zhaohong1   

  1. 1(School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122);2(School of Transportation and Information, Hubei Communications Technical College, Wuhan 430079)
  • Online: 2017-09-01

摘要: 属性效应在现实生活中广泛存在,如果不加以控制,将会严重影响回归学习的性能.针对大规模数据属性效应控制的非线性回归学习问题,提出了快速等均值核心向量回归机(fast equal mean-core vector regression, FEM-CVR).首先基于间隔最大化目标学习准则,通过施加等均值约束条件,提出了等均值支持向量回归机(equal mean-support vector regression, EM-SVR).在此基础上,证明了EM-SVR等价于一个中心约束最小包含球(center constrained-minimum enclosing ball, CC-MEB)问题,然后通过引入近似最小包含球理论,得到原始输入数据集的压缩集即核心集(core set),进一步提出了针对大规模数据属性效应控制的最小包含球快速非线性回归学习方法FEM-CVR,并从理论上对相关性质进行了深入分析.实验表明:该方法能够快速处理针对大规模数据属性效应控制的非线性回归学习问题,具有较好的泛化能力,并且其时间复杂度上限与数据集大小无关,仅与最小包含球近似参数ε有关.

关键词: 回归学习, 属性效应控制, 中心约束最小包含球, 等均值约束, 大规模数据

Abstract: Attribute effect is a kind of phenomenon of data bias caused by sensitive attributes, which widely exists in real world. If not controlled, it will seriously affect the learning performance of regression model. In order to control the attribute effect in nonlinear regression model on large scale biased dataset, a novel fast equal mean-core vector regression (FEM-CVR) is proposed. First, a novel equal mean-support vector regression (EM-SVR) based on margin maximization criterion is proposed by using the constraint condition of equal mean. On this basis, the fact that the optimization problem of EM-SVR is equivalent to a center constrained-minimum enclosing ball (CC-MEB) problem is derived. Then a novel fast minimum enclosing ball based nonlinear regression learning algorithm for attribute effect control on large scale biased dataset, referred to as FEM-CVR, is further proposed by integrating the approximate minimum enclosing ball theory and reducing the original input dataset into the core set. In addition, some fundamental theoretical properties are deeply discussed. Finally, extensive experiments are conducted on synthetic and real datasets, and experimental results show that our FEM-CVR can effectively control attribute effect in nonlinear regression model on large scale biased dataset with good generalization ability, whose upper bound of the time complexity is independent of the size of the dataset, only related to the approximate parameter of the minimum enclosing ball ε.

Key words: regression learning, attribute effect control, center constrained-minimum enclosing ball (CC-MEB), equal mean constraint, large scale data

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