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

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Abstract

Block principle component analysis (BPCA) is a recently developed technique in computer vision and pattern classification. 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 classification and reconstruction on several benchmark sets show the effectiveness of the proposed approach.

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Authors and Affiliations

  1. School of Computer and Information, Anhui Polytechnic University, Wuhu, 241000, Anhui, China

    Ganyi Tang  (唐肝翌) & Guifu Lu  (卢桂馥)

Authors
  1. Ganyi Tang  (唐肝翌)
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  2. Guifu Lu  (卢桂馥)
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Corresponding author

Correspondence to Ganyi Tang  (唐肝翌).

Additional information

Foundation item: the National Natural Science Foundation of China (No. 61572033), the Natural Science Foundation of Education Department of Anhui Province of China (No. KJ2015ZD08), and the Higher Education Promotion Plan of Anhui Province of China (No. TSKJ2015B14)

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Tang, G., Lu, G. Block Principle Component Analysis with Lp-norm for Robust and Sparse Modelling. J. Shanghai Jiaotong Univ. (Sci.) 23, 398–403 (2018). https://doi.org/10.1007/s12204-018-1955-4

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  • DOI: https://doi.org/10.1007/s12204-018-1955-4

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