DocumentCode :
47794
Title :
Robust 2DPCA With Non-greedy 
-Norm Maximization for Image Analysis
Author :
Rong Wang ; Feiping Nie ; Xiaojun Yang ; Feifei Gao ; Minli Yao
Author_Institution :
Xi´an Res. Inst. of Hi-Tech, Xi´an, China
Volume :
45
Issue :
5
fYear :
2015
fDate :
May-15
Firstpage :
1108
Lastpage :
1112
Abstract :
2-D principal component analysis based on ℓ1-norm (2DPCA-L1) is a recently developed approach for robust dimensionality reduction and feature extraction in image domain. Normally, a greedy strategy is applied due to the difficulty of directly solving the ℓ1-norm maximization problem, which is, however, easy to get stuck in local solution. In this paper, we propose a robust 2DPCA with non-greedy ℓ1-norm maximization in which all projection directions are optimized simultaneously. Experimental results on face and other datasets confirm the effectiveness of the proposed approach.
Keywords :
feature extraction; image processing; optimisation; principal component analysis; 2D principal component analysis; feature extraction; greedy strategy; image analysis; nongreedy ℓ1-norm maximization; projection directions; robust 2DPCA; robust dimensionality reduction; Databases; Face; Face recognition; Principal component analysis; Robustness; Training; Vectors; ${ell _{1}}$ -norm; ℓ₁-norm}; 2-D principal component analysis (2DPCA); non-greedy strategy; outliers; principal component analysis (PCA);
fLanguage :
English
Journal_Title :
Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
2168-2267
Type :
jour
DOI :
10.1109/TCYB.2014.2341575
Filename :
6884824
Link To Document :
