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

作　　者：TANG Ganyi LU Guifu 唐肝翌;卢桂馥(School of Computer and Information, Anhui Polytechnic University)

机构地区：School of Computer and Information, Anhui Polytechnic University, Wuhu 241000, Anhui, China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2018年第3期398-403,共6页上海交通大学学报（英文版）

基　　金：the National Natural Science Foundation of China(No.61572033);the Natural Science Foundation of Education Department of Anhui Province of China(No.KJ2015ZD08);the Higher Education Promotion Plan of Anhui Province of China(No.TSKJ2015B14)

摘　　要：Block principle and pattern classification component analysis （BPCA） is a recently developed technique in computer vision 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.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.

关 键 词：block principle component analysis(BPCA) LP-NORM robust modelling sparse modelling 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]