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A new sparse representation-based classification algorithm using iterative class elimination

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Abstract

In this paper, we propose a sparse representation-based classification method using iterative class elimination strategy for face recognition. The proposed method aims to represent a test sample as a linear combination of the most competitive training samples and exploits an optimal representation of training samples from the classes with major relevant contributions. We interpret the sparse representation problem as an information fidelity problem. In the context of our proposed method, an important goal is to select a subset of variables for accomplishing one objective: the provision of a descriptive representation for sparse class knowledge structure. We develop an iterative class elimination algorithm to achieve this goal. First, the contribution in presenting the test sample of any of the specified classes is, respectively, calculated by adding up the total contribution of all training samples of this class, and then a certain class that meets the smallest score requirement to this test sample is eliminated. Second, a similar procedure is iteratively carried out for the set of remaining training samples from rest classes, and this procedure is repeatedly performed till the predefined termination condition is satisfied. The final remaining training samples are used to produce a best representation of the test sample and to classify it. Therefore, the proposed algorithm is an iterative method that alternates between sparse representation and a process of updating the training atoms to better fit the test data. This is helpful to accurately classify the test sample. Experimental results conducted on the ORL, FERET, and AR face databases demonstrate the effectiveness of the proposed method.

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Acknowledgments

This work was supported by the National Science Foundation of China (Grant No. 61100116), the Natural Science Foundation of Jiangsu Province (Grant Nos. BK2012700 and BK2011492), the China Postdoctoral Science Foundation (Grant No. 2011M500926), the Jiangsu Postdoctoral Science Foundation (Grant No. 1102063C), the Foundation of Artificial Intelligence Key Laboratory of Sichuan Province (Grant No. 2012RZY02) and the Foundation of Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, People’s Republic of China

    Xiaoning Song, Xibei Yang & Shang Gao

  2. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Xiaoning Song, Zi Liu, Xibei Yang & Shang Gao

  3. Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, 230039, People’s Republic of China

    Xiaoning Song

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  1. Xiaoning Song
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  2. Zi Liu
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Correspondence to Xiaoning Song.

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Song, X., Liu, Z., Yang, X. et al. A new sparse representation-based classification algorithm using iterative class elimination. Neural Comput & Applic 24, 1627–1637 (2014). https://doi.org/10.1007/s00521-013-1399-6

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