A convergent solution to two dimensional linear discriminant analysis

Authors:
Wei Chen;Kaiqi Huang;Tieniu Tan;Dacheng Tao
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, P.R.China;National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, P.R.China;National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, P.R.China;School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

The matrix based data representation has been recognized to be effective for face recognition because it can deal with the undersampled problem. One of the most popular algorithms, the two dimensional linear discriminant analysis (2DLDA), has been identified to be effective to encode the discriminative information for training matrix represented samples. However, 2DLDA does not converge in the training stage. This paper presents an evolutionary computation based solution, referred to as E-2DLDA, to provide a convergent training stage for 2DLDA. In E-2DLDA, every randomly generated candidate projection matrices are first normalized. The evolutionary computation method optimizes the projection matrices to best separate different classes. Experimental results show E- 2DLDA is convergent and outperforms 2DLDA.