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Robust classifier using distance-based representation with square weights

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Abstract

This paper presents a new multi-class classification method that is different from sparse representation classifier (SRC) method. SRC is a classical method which has been widely used for face recognition and digit identification. However, SRC method only looks for the sparsest solution using \(l_1\) norm minimization with high computation complexity. The sparsest representation cannot show the space distribution feature of samples. Moreover, the sparsest representation does not mean obtaining the highest recognition rate for data classification. This paper proposes a distance-based representation method for classification. The distance between samples is used to measure the similarity. It is crucial that square weights \(x_i^2\) are used as the weight of distance instead of \(x_i\). Furthermore, a closed form solution is obtained so that the computation complexity is lower than that of SRC. The extensive experiments show that the proposed method achieves very competitive classification results.

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Acknowledgments

This work was supported by Specialized Research Fund for the Doctoral Program of Higher Education under Grant 2010081110053 and National Program on Key Basic Research Project (973 Program) under Grant 2011CB302201, partially supported by National Science Foundation of China under grant 61375065.

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Authors and Affiliations

  1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu , 610065, People’s Republic of China

    Jiangshu Wei, Jian Cheng Lv & Zhang Yi

Authors
  1. Jiangshu Wei
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  2. Jian Cheng Lv
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  3. Zhang Yi
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Corresponding author

Correspondence to Jian Cheng Lv.

Additional information

Communicated by A. Castiglione.

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Wei, J., Lv, J.C. & Yi, Z. Robust classifier using distance-based representation with square weights. Soft Comput 19, 507–515 (2015). https://doi.org/10.1007/s00500-014-1272-2

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