Static and incremental robust kernel factorization embedding graph regularization supporting ill-conditioned industrial data recovery
作者：
Highlights：
• Static and incremental data recovery methods are proposed.
• Kernel factorization, sparse norm and graph regularization are integrated.
• Distributed updating strategy of parameters is proposed.
• The maximum missing rate of the proposed methods is given.
• Real industrial processes show that our algorithms have higher recoverability.
摘要
•Static and incremental data recovery methods are proposed.•Kernel factorization, sparse norm and graph regularization are integrated.•Distributed updating strategy of parameters is proposed.•The maximum missing rate of the proposed methods is given.•Real industrial processes show that our algorithms have higher recoverability.
论文关键词：Ill-conditioned data recovery,Incremental robust kernel factorization,Graph regularization,Distributed adaptive proximal Newton gradient descent
论文评审过程：Received 20 May 2022, Revised 12 September 2022, Accepted 25 September 2022, Available online 5 October 2022, Version of Record 12 October 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118921
