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Neural Computing and Applications

, Volume 31, Issue 3, pp 887–893 | Cite as

Sparse coding network model based on fast independent component analysis

  • Guanzheng Wang
  • Rubin WangEmail author
Original Article
  • 100 Downloads

Abstract

Neurobiological studies have shown that neurons in the primary visual cortex (V1) may employ sparse presentations to represent stimuli. We describe a network model for sparse coding which includes input layer, base functional layer and output layer. We simulated standard sparse coding and sparse coding based on fast independent component analysis (ICA). The duration of training bases, the convergence speed of objective function and the sparsity of coefficient matrix were compared, respectively. The results show that sparse coding based on fast ICA is more effective than standard sparse coding.

Keywords

Sparse coding Independent component analysis Bases Natural images Sparsity 

Notes

Compliance with ethical standards

Conflict of interest

The authors do not have any type of conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Institute for Cognitive NeurodynamicsEast China University of Science and TechnologyShanghaiChina

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