L1-norm-based (2D)2PCA

摘要：

　　Traditionai bidirectional two-dimension (2D)principal component analysis ((2D)2PCA-L2) is sensitive to outliers because its objective function is the least squares criterion based on L2-norm.This paper proposes a simple but effective Ll-norm-based bidirectional 2D principal component analysis ((2D)2PCA-L1),which jointly takes advantage of the merits of bidirectional 2D subspace learning and L1-norm-based distance criterion.Experimental results on two popular face databases show that the proposed method is more robust to outliers than several methods based on principal component analysis in the fields of data compression and object recognition.

关键词： bidirectional two-dimension principal component analysis l2-norm outliers L1-norm Optimization

作者: Fujin Zhong

作者单位: School of Computer & Information Engineering Yibin University name of organization Yibin,China;School of Information Science & Technology Southwest Jiaotong University Chengdu,China

会议类型: 国际会议

会议名称: 2013 2nd International Conference on Computer Science and Electronics Engineering(ICCSEE2013)(2013年第二届计算机科学与电子工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 1294-1297

在线出版日期: 2013-03-22（万方平台首次上网日期，不代表论文的发表时间）