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

页码：568 / 573

页数：6

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