Feature Extraction Based on Maximum Nearest Subspace Margin Criterion

Authors:
Yi Chen;Zhenzhen Li;Zhong Jin
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, People's Republic of China 210094;School of Information Engineering, Jiangxi Manufacturing Technology College, Nanchang, China 330095;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, People's Republic of China 210094
Venue:
Neural Processing Letters
Year:
2013

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Abstract

Based on the classification rule of sparse representation-based classification (SRC) and linear regression classification (LRC), we propose the maximum nearest subspace margin criterion for feature extraction. The proposed method can be seen as a preprocessing step of SRC and LRC. By maximizing the inter-class reconstruction error and minimizing the intra-class reconstruction error simultaneously, the proposed method significantly improves the performances of SRC and LRC. Compared with linear discriminant analysis, the proposed method avoids the small sample size problem and can extract more features. Moreover, we extend LRC to overcome the potential singular problem. The experimental results on the extended Yale B (YALE-B), AR, PolyU finger knuckle print and the CENPARMI handwritten numeral databases demonstrate the effectiveness of the proposed method.