Incomplete multi-view clustering via virtual-label guided matrix factorization

作者：

Highlights：

• We present a novel one-step incomplete multi-view clustering method.

• We use the virtual label information to guide the multi-view learning.

• We design a parameter-free multi-view graph regularization.

• We develop an efficient optimization algorithm.

摘要

•We present a novel one-step incomplete multi-view clustering method.•We use the virtual label information to guide the multi-view learning.•We design a parameter-free multi-view graph regularization.•We develop an efficient optimization algorithm.

论文关键词：Incomplete views,Virtual label,Consensus latent representation,Matrix factorization

论文评审过程：Received 25 February 2022, Revised 23 July 2022, Accepted 3 August 2022, Available online 8 August 2022, Version of Record 17 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118408