Feature Extraction by Correntropy Based Average Neighborhood Margin Maximization

摘要：

　　Average neighborhood margin maximization(ANMM)is a feature extraction method to make homogeneous points collect as near as possible and heterogeneous points disperse as far away as possible.To enhance the anti-noise ability of ANMM,correntropy based average neighborhood margin maximization(CANMM)is proposed in this paper.This method utilizes correntropy to substitute the Euclidean distance for measuring the similarity between the given data,and uses the maximum correntropy criterion to replace the maximum distance criterion,which makes CANMM more robust.The experimental results on three benchmark face databases validate the effectiveness of the proposed method.

关键词： Feature Extraction Half-quadratic optimization Correntropy ANMM

作者: Lin-Na Ma Hong-Jie Xing Shun-Yan Hou

作者单位: Key Laboratory of Machine Learning and Computational Intelligence,College of Mathematics and Compute College of Electronics and Information Engineering,Hebei University,Baoding 071002,China

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 2616-2620

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）