摘要
在传统线性关联向量机的基础上,设计了一种多特征融合的多类分类器.该分类器基于多类Probit回归模型将传统的两类线性关联向量机推广为多类关联向量机,利用线性关联向量机的特征选择功能,对融合的高维特征向量进行降维和合理的幂次扩展,使线性关联向量机具有构造非线性分类界面的能力,以保证对非线性多类分类问题稳健的融合识别性能.针对雷达高分辨距离像目标识别问题,提取3种平移不变特征,使用提出的多特征融合的多类分类器在基于实测数据的识别实验中得到了稳健的融合识别结果.
A multi-class classifier for feature fusion is designed based on the traditional linear Relevance Vector Machine（RVM）.The proposed classifier extends the binary RVM to multi-class RVM based on the multinomial Probit regression model,utilizes the feature selection property of the linear RVM to reduce the dimensionality of the fused feature vector,and makes the linear RVM framework have the ability to form the nonlinear classification boundary via the rational power extension.All of these properties can ensure the robust fusion recognition performance in the case of nonlinear multi-class classification.For the application of radar target recognition using the high-resolution range profile（HRRP）,the experimental results on the measured data show that the proposed multi-class classifier with the three translation-invariant features extracted from HRRP data can achieve robust recognition performance.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2013年第1期111-117,共7页
Journal of Xidian University
基金
国家自然科学基金资助项目(60901067)
新世纪优秀人才支持计划资助项目(NCET-09-0630)
长江学者和创新团队发展计划资助项目(IRT0954)
全国博士学位论文作者专项资金资助项目(FANEDD-201156)
关键词
关联向量机
多类分类器
特征融合
特征选择
高分辨距离像
雷达目标识别
relevance vector machine
multi-class classifier
feature fusion
feature selection
high-resolution range profile
radar target recognition
