Joint Feature Selection and Classifier Design for Radar Targets

摘要：

A joint feature selection and classifier design is proposed in this paper. The approach adopts the feature dimension extension by power transformation as a new kernel function, which can not only make full use of the input samples to form the nonlinear classification boundary, but also realize the nonlinear feature selection. A zero-mean Gaussian prior with Gamma precision is used to promote sparsity in utilization of features in our model. The experiments based on a measured radar data set demonstrate the practicability and effectiveness of the proposed method.

关键词： classifier design feature selection sparsity Relevance Vector Machine (RVM) variational bayesian (VB) inference.

作者: Danlei Xu Lan Du Hongwei Liu

作者单位: National Laboratory of Radar Signal Processing, Xidian University, Xi’an, China

会议类型: 国际会议

会议名称: 2011 IEEE CIE International Conference on Radar(2011年IEEE国际雷达会议RADAR 2011)

会议地点: 成都

会议语种:英文

页码: 617-620

在线出版日期: 2011-10-24（万方平台首次上网日期，不代表论文的发表时间）