Title :
Robust two-dimensional principle component analysis
Author :
Chunming, Xu ; Haibo, Jiang ; Jianjiang, Yu
Author_Institution :
Sch. of Math. Sci., Yancheng Teachers Univ., Yancheng
Abstract :
Two-dimensional principle component analysis (2DPCA) is a popular and fast feature extraction method. However, it doesnpsilat consider the outliers of the training samples. To address this problem, we present robust two-dimensional principle component analysis algorithm (R2DPCA), which gives a new weighted method for the evaluation of the total squared error. The solution for R2DPCA is also given. The proposed method is tested on AR face database and ORL face database, and the experimental results indicate that it is more effective than two-dimensional principle component analysis (2DPCA).
Keywords :
feature extraction; principal component analysis; visual databases; AR face database; ORL face database; feature extraction method; robust two-dimensional principle component analysis; Algorithm design and analysis; Face recognition; Feature extraction; Information analysis; Information science; Pareto analysis; Robustness; Spatial databases; Speckle; Testing; Face Recognition; Feature Extraction; Principle Component Analysis (PCA); Robust Two-dimensional Principle Component Analysis (R2DPCA); Two-dimensional Principle Component Analysis (2DPCA);
Conference_Titel :
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location :
Kunming
Print_ISBN :
978-7-900719-70-6
Electronic_ISBN :
978-7-900719-70-6
DOI :
10.1109/CHICC.2008.4605066
