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.
TANG Ganyi (唐肝翌), LU Guifu (卢桂馥)
. Block Principle Component Analysis with Lp-norm for Robust and Sparse Modelling[J]. Journal of Shanghai Jiaotong University(Science), 2018
, 23(3)
: 398
.
DOI: 10.1007/s12204-018-1955-4
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