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

作     者：TANG Ganyi LU Guifu 唐肝翌;卢桂馥

作者机构：School of Computer and Information Anhui Polytechnic University Wuhu 241000 Anhui China 

出 版 物：《Journal of Shanghai Jiaotong university(Science)》 (上海交通大学学报（英文版）)

年 卷 期：2018年第23卷第3期

页      面：398-403页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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) 

主　　题：block principle component analysis(BPCA) Lp-norm robust modelling sparse modelling 

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.