Robust Sparse 2D Principal Component Analysis for Object Recognition

Authors

Jicheng Meng, School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. ChinaFollow
Xiaolong Zheng

Author Country (or Countries)

P.R. China

Abstract

We extensively investigate robust sparse two dimensional principal component analysis (RS2DPCA) that makes the best of semantic, structural information and suppresses outliers in this paper. The RS2DPCA combines the advantages of sparsity, 2D data format and L1-norm for data analysis. We also prove that RS2DPCA can offer a good solution of seeking spare 2D principal components. To verify the performance of RS2DPCA in object recognition, experiments are performed on three famous face databases, i.e. Yale, ORL, and FERET, and the experimental results show that the proposed RS2DPCA outperform the same class of algorithms for face recognition, such as robust sparse PCA, L1-norm-based 2DPCA.

Suggested Reviewers

N/A

Recommended Citation

Meng, Jicheng and Zheng, Xiaolong (2013) "Robust Sparse 2D Principal Component Analysis for Object Recognition," Applied Mathematics & Information Sciences: Vol. 07: Iss. 6, Article 45.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol07/iss6/45