KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Two Dimensional Slow Feature Discriminant Analysis via L2,1 Norm Minimization for Feature Extraction

  • Gu, Xingjian (College of Information Science and Technology, Nanjing Agricultural University) ;
  • Shu, Xiangbo (School of Computer Science and Engineering, Nanjing University of Science and Technology) ;
  • Ren, Shougang (College of Information Science and Technology, Nanjing Agricultural University) ;
  • Xu, Huanliang (College of Information Science and Technology, Nanjing Agricultural University)
  • Received : 2017.09.12
  • Accepted : 2018.02.06
  • Published : 2018.07.31
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Abstract

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method inspired by biological mechanism. In this paper, a novel method called Two Dimensional Slow Feature Discriminant Analysis via $L_{2,1}$ norm minimization ($2DSFDA-L_{2,1}$) is proposed. $2DSFDA-L_{2,1}$ integrates $L_{2,1}$ norm regularization and 2D statically uncorrelated constraint to extract discriminant feature. First, $L_{2,1}$ norm regularization can promote the projection matrix row-sparsity, which makes the feature selection and subspace learning simultaneously. Second, uncorrelated features of minimum redundancy are effective for classification. We define 2D statistically uncorrelated model that each row (or column) are independent. Third, we provide a feasible solution by transforming the proposed $L_{2,1}$ nonlinear model into a linear regression type. Additionally, $2DSFDA-L_{2,1}$ is extended to a bilateral projection version called $BSFDA-L_{2,1}$. The advantage of $BSFDA-L_{2,1}$ is that an image can be represented with much less coefficients. Experimental results on three face databases demonstrate that the proposed $2DSFDA-L_{2,1}/BSFDA-L_{2,1}$ can obtain competitive performance.

Keywords

References

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