Auto-weighted sample-level fusion with anchors for incomplete multi-view clustering
作者：
Highlights：
• highlights
• An anchor graph based method is proposed for incomplete multi-view clustering.
• It can be applied to large-scale multi-view datasets.
• The instance-to-anchor similarity fusion is implemented at the sample level in an auto-weighted way to get the optimal weight for the corresponding view of each sample.
• Experiments on 32 datasets with eight baselines show the superiority of our method. Even on datasets with only 10% complete samples, ASA-IC still performs well.
摘要
highlights•An anchor graph based method is proposed for incomplete multi-view clustering.•It can be applied to large-scale multi-view datasets.•The instance-to-anchor similarity fusion is implemented at the sample level in an auto-weighted way to get the optimal weight for the corresponding view of each sample.•Experiments on 32 datasets with eight baselines show the superiority of our method. Even on datasets with only 10% complete samples, ASA-IC still performs well.
论文关键词：Incomplete data,Multi-view clustering,Anchor,Auto-weighted,Large-scale
论文评审过程：Received 2 March 2021, Revised 16 March 2022, Accepted 1 May 2022, Available online 8 May 2022, Version of Record 26 May 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108772
