sa ›› 2018, Vol. 23 ›› Issue (3): 398-.doi: 10.1007/s12204-018-1955-4

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Block Principle Component Analysis with Lp-norm for Robust and Sparse Modelling

TANG Ganyi (唐肝翌), LU Guifu (卢桂馥)   

  1. (School of Computer and Information, Anhui Polytechnic University, Wuhu 241000, Anhui, China)
  • 出版日期:2018-05-31 发布日期:2018-06-17
  • 通讯作者: TANG Ganyi (唐肝翌) E-mail:tangganyi.tony@qq.com

Block Principle Component Analysis with Lp-norm for Robust and Sparse Modelling

TANG Ganyi (唐肝翌), LU Guifu (卢桂馥)   

  1. (School of Computer and Information, Anhui Polytechnic University, Wuhu 241000, Anhui, China)
  • Online:2018-05-31 Published:2018-06-17
  • Contact: TANG Ganyi (唐肝翌) E-mail:tangganyi.tony@qq.com

摘要: Block principle component analysis (BPCA) is a recently developed technique in computer vision and pattern classiˉcation. In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, which inherits the robustness of BPCA-L1 due to the employment of adjustable Lp-norm. In order to perform a sparse modelling, the elastic net is integrated into the objective function. An iterative algorithm which extracts feature vectors one by one greedily is elaborately designed. The monotonicity of the proposed iterative procedure is theoretically guaranteed. Experiments of image classiˉcation and reconstruction on several benchmark sets show the e?ectiveness of the proposed approach.

关键词: block principle component analysis (BPCA), Lp-norm, robust modelling, sparse modelling

Abstract: Block principle component analysis (BPCA) is a recently developed technique in computer vision and pattern classiˉcation. In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, which inherits the robustness of BPCA-L1 due to the employment of adjustable Lp-norm. In order to perform a sparse modelling, the elastic net is integrated into the objective function. An iterative algorithm which extracts feature vectors one by one greedily is elaborately designed. The monotonicity of the proposed iterative procedure is theoretically guaranteed. Experiments of image classiˉcation and reconstruction on several benchmark sets show the e?ectiveness of the proposed approach.

Key words: block principle component analysis (BPCA), Lp-norm, robust modelling, sparse modelling

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