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Batch Dictionary Learning with Augmented Orthogonal Matching Pursuit

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

Abstract

Dictionary learning is often incorporated in classification method, which can obtain a new representation under the learned dictionary to achieve better classification performance. In this paper, we propose a novel Batch Dictionary Learning model with augmented orthogonal matching pursuit classification. Batch Dictionary Learning model is capable of improving the dictionary by removing the redundancy of over-complete dictionary, thus the learned optimal dictionary is more suitable for classification. To solve the optimization target, we improve the traditional orthogonal matching pursuit (OMP) algorithm and propose an augmented orthogonal matching pursuit algorithm (AOMP) to solve the objective function. Superior experimental results demonstrate that our proposed model outperform the other state-of-the-art classification algorithms on real-world dataset.

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, 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

  • Ping He
    • 1
  • Baichuan Fan
    • 1
  • Xiaohua Xu
    • 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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