Funding for this research was provided by:
Engineering and Physical Sciences Research Council (EP/J017515/1)
National Natural Science Foundation of China (61329302, 61432012, 61625204, 61971296, U19A2078)
Science, Technology and Innovation Commission of Shenzhen Municipality (ZDSYS201703031748284)
Shenzhen Peacock Plan (KQTD2016112514355531)
Ministry of Education-China Mobile Research Fund Project (MCM20180405)
Program for University Key Laboratory of Guangdong Province (2017KSYS008)
This article is maintained by: Elsevier
Article Title: Kernel truncated regression representation for robust subspace clustering
Journal Title: Information Sciences
CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.ins.2020.03.033
Content Type: article
Copyright: © 2020 Elsevier Inc. All rights reserved.