NAGSC: NESTEROV’S ACCELERATED GRADIENT METHODS FOR SPARSE CODING

sparse coding

Authors

  • Liang Liu Chongqing University of Posts and Telecommunications
  • Ling Zhang Chongqing Industry Polytechnic College
  • Xiangguang Dai Chongqing Three Gorges University - Key Laboratory of Intelligent Information Processing and Control
  • Yuming Feng Chongqing Three Gorges University - Engineering Research Center of Internet of Things and Intelligent Control Technology

Keywords:

sparse coding, nonsmooth, nonconvex, accelerated gradient

Abstract

This paper proposes efficient algorithms for Sparse Coding.  Firstly, Sparse Coding is divided into two sub-convex problems including L1 and L2 prob-lems.  Secondly, we transform the nonsmooth L1 problem into two smooth sub-problems, and alternatively optimize them by Nesterov’s Accelerated Gradient methods (NAG).  Thirdly, we apply NAG to optimize L2 problem.  Finally, L1 and L2 problems are iteratively solved until convergence.  Experiments show that our proposed algorithms are effective to optimize L1, L2 and learn over-complete bases.

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Published

2018-07-31

How to Cite

Liu, L., Zhang, L., Dai, X., & Feng, Y. (2018). NAGSC: NESTEROV’S ACCELERATED GRADIENT METHODS FOR SPARSE CODING: sparse coding. Italian Journal of Pure and Applied Mathematics, 40, 724–735. Retrieved from https://journals.uniurb.it/index.php/ijpam/article/view/6966

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Section

Articoli - Forum Editrice

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