文献类型：期刊文献

英文题名：Robust auto-weighted projective low-rank and sparse recovery for visual representation

作者：Wang, Lei[1] Wang, Bangjun[1] Zhang, Zhao[1,2,3] Ye, Qiaolin[4] Fu, Liyong[5] Liu, Guangcan[6] Wang, Meng[2,3]

第一作者：Wang, Lei

通信作者：Zhang, Z[1];Zhang, Z[2];Zhang, Z[3];Fu, LY[4]

机构：[1]Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China;[2]Hefei Univ Technol, Sch Comp Sci, Hefei, Anhui, Peoples R China;[3]Hefei Univ Technol, Sch Artificial Intelligence, Hefei, Anhui, Peoples R China;[4]Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Jiangsu, Peoples R China;[5]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[6]Nanjing Univ Informat Sci & Technol, Sch Informat & Control, Nanjing, Jiangsu, Peoples R China

年份：2019

卷号：117

起止页码：201-215

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20192307007901);Scopus(收录号：2-s2.0-85066465611);WOS:【SCI-EXPANDED(收录号:WOS:000477943300013)】；

基金：The authors would like to express sincere thanks to reviewers for their insightful comments, making our manuscript a higher standard. This work is partially supported by the Central Public-interest Scientific Institution Basal Research Fund (CAFYBB2019QD003), National Natural Science Foundation of China (61672365, 61732008, 61725203, 61622305, 61871444, 61572339), Natural Science Foundation of Jiangsu Province of China (BK20160040), Fundamental Research Funds for the Central Universities of China (JZ2019HGPA0102) and the High-Level Talent of "Six Talent Peak" Project of Jiangsu Province of China (XYDXX-055).

语种：英文

外文关键词：Auto-weighted low-rank and sparse recovery; Robust representation; Feature extraction; Classification

摘要：Most existing low-rank and sparse representation models cannot preserve the local manifold structures of samples adaptively, or separate the locality preservation from the coding process, which may result in the decreased performance. In this paper, we propose an inductive Robust Auto-weighted Low-Rank and Sparse Representation (RALSR) framework by joint feature embedding for the salient feature extraction of high-dimensional data. Technically, the model of our RALSR seamlessly integrates the joint low-rank and sparse recovery with robust salient feature extraction. Specifically, RALSR integrates the adaptive locality preserving weighting, joint low-rank/sparse representation and the robustness-promoting representation into a unified model. For accurate similarity measure, RALSR computes the adaptive weights by minimizing the joint reconstruction errors over the recovered clean data and salient features simultaneously, where L1-norm is also applied to ensure the sparse properties of learnt weights. The joint minimization can also potentially enable the weight matrix to have the power to remove noise and unfavorable features by reconstruction adaptively. The underlying projection is encoded by a joint low-rank and sparse regularization, which can ensure it to be powerful for salient feature extraction. Thus, the calculated low-rank sparse features of high-dimensional data would be more accurate for the subsequent classification. Visual and numerical comparison results demonstrate the effectiveness of our RALSR for data representation and classification. (C) 2019 Elsevier Ltd. All rights reserved.