一种基于变分相关向量机的特征选择和分类结合方法

徐丹蕾; 杜兰; 刘宏伟; 洪灵; 李彦兵

doi:10.3724/SP.J.1004.2011.00932

一种基于变分相关向量机的特征选择和分类结合方法

doi: 10.3724/SP.J.1004.2011.00932

1.
西安电子科技大学雷达信号处理国家重点实验室西安 710071

详细信息

通讯作者:
杜兰西安电子科技大学电子工程学院教授. 2007年获得西安电子科技大学信息与通信工程博士学位, 主要研究方向为统计信号处理、雷达信号处理、机器学习及其在雷达目标检测与识别方面的应用.本文通信作者.E-mail: dulan@mail.xidian.edu.cn

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出版历程
- 收稿日期: 2010-09-16
- 修回日期: 2011-02-01
- 刊出日期: 2011-08-20

Joint Feature Selection and Classification Design Based on Variational Relevance Vector Machine

1.
National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071

摘要

摘要: 相关向量机(Relevance vector machine, RVM)是一种函数形式等价于支持向量机(Support vector machine, SVM)的全概率模型,利用变分贝叶斯(Variational Bayesian, VB)方法求解的RVM可以给出所有参数的后验分布. 进一步,通过对样本所在原始特征空间的稀疏化,基于线性核的RVM可以在分类的同时实现对原始特征的线性选择. 本文在传统VB-RVM的基础上提出一种特征选择和分类结合方法. 该方法采用Probit模型将分类问题与回归问题有机地结合起来, 同时,通过对特征维的幂变换扩展,不仅在分类时增加了样本的信息量, 可以构造非线性分类面,而且实现了非线性特征选择的功能. 通过对仿真数据和实测数据分别进行实验, 证明了该特征选择和分类结合方法的实用性和有效性.
- 特征选择 /
- 稀疏化 /
- 相关向量机 /
- Probit模型 /
- 变分贝叶斯
Abstract: The relevance vector machine (RVM) is a fully probabilistic model with an equivalent functional form as the support vector machine (SVM), which can give posterior distributions over all parameters through the variational Bayesian (VB) method. Moreover, the RVM with linear kernel can realize not only classification but also linear feature selection by imposing sparsity in feature space where data is originally represented. In this paper, a joint feature selection and classification design is proposed based on the traditional VB-RVM. In the proposed framework, the Probit model is utilized to connect the regression problem with the classification problem, and the feature dimension extension by power transformation can make full use of the samples form the nonlinear classification boundary, and can realize nonlinear feature selection as well. The experiments based on the synthetic data and measured data demonstrate the practicability and effectiveness of the proposed method.
- Feature selection /
- sparsity /
- relevance vector machine (RVM) /
- Probit model /
- variational Bayesian (VB)

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参考文献(1)

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