Robust Tensor Principal Component Analysis by Lp-Norm for Image Analysis

被引：0
|
作者：
Tang, Ganyi [1 ]
Lu, Guifu [2 ]
Wang, Zhongqun [3 ]
Xie, Yukai [2 ]
机构：
[1] Anhui Polytech Univ, AHPU, Sch Comp & Informat, Wuhu, Peoples R China
[2] Anhui Polytech Univ, AHPU, Sch Comp Sci & Informat, Wuhu, Peoples R China
[3] Anhui Polytech Univ, AHPU, Sch Management Engn, Wuhu, Peoples R China
来源：
2016 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC) | 2016年
基金：
中国国家自然科学基金;
关键词：
tensor; principal component analysis ( PCA); TPCA; Lp-norm; outlies; MAXIMIZATION; L1-NORM;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Tensor principal component analysis (TPCA), which can make full use of the spatial relationship of images/videos, is a generalization of the classical principal component analysis (PCA). However, the existing TPCA method is based on the Frobenius-norm, which makes it sensitive to outliers. In order to overcome the drawback of TPCA, in this paper, we proposed a novel Lp-norm based TPCA (TPCA-Lp), which is robust to outliers. We also designed an iterative algorithm to solve the optimization of TPCA-Lp, in which all projection matrices are optimized by turns. Experimental results upon several face databases demonstrate the advantages of the proposed approach.
引用
收藏
页码：568 / 573
页数：6
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