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Two-Sided Sparse Learning with Augmented Lagrangian Method

  • Xiaohua Xu
  • Baichuan Fan
  • Ping HeEmail author
  • Yali Liang
  • Yuan Lou
  • Zhijun Zhang
  • Xincheng Chang
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

In this paper, we propose a novel sparse learning model, named Two-Sided Sparse Learning with Augmented Lagrangian Method, and apply it to the classification problem. Existing dictionary learning method only emphasizes the sparsity of cases, but neglect the sparsity of features. In the context of classification, it is crucial to take into account the correlation among features and find the most representative features in a class. By representing training data as sparse linear combination of rows and columns in dictionary, this model can be more suitable for classification problem. Experimental results demonstrate that our model achieves superior performance than the state-of-the-art classification methods on real-world datasets.

Keywords

Classification Sparse learning Dictionary learning Sparse representation 

Notes

Acknowledgements

This research was supported in part by the Chinese National Natural Science Foundation under Grant nos. 61402395, 61472343 and 61502412, Natural Science Foundation of Jiangsu Province under contracts BK20140492, BK20151314 and BK20150459, Jiangsu overseas research and training program for university prominent young and middle-aged teachers and presidents, Jiangsu government scholarship funding.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Xiaohua Xu
    • 1
  • Baichuan Fan
    • 1
  • Ping He
    • 1
    Email author
  • Yali Liang
    • 1
  • Yuan Lou
    • 1
  • Zhijun Zhang
    • 1
  • Xincheng Chang
    • 1
  1. 1.Department of Computer ScienceYangzhou UniversityYangzhouChina

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