Robust Multi-View Clustering With Incomplete Information
Sichuan University · Chengdu University
Abstract
The success of existing multi-view clustering methods heavily relies on the assumption of view consistency and instance completeness, referred to as the complete information. However, these two assumptions would be inevitably violated in data collection and transmission, thus leading to the so-called Partially View-unaligned Problem (PVP) and Partially Sample-missing Problem (PSP). To overcome such incomplete information challenges, we propose a novel method, termed robuSt mUlti-view clusteRing with incomplEte information (SURE), which solves PVP and PSP under a unified framework. In brief, SURE is a novel contrastive learning paradigm which uses the available pairs as positives and randomly chooses some…
Citation impact
- FWCI
- 28.21
- Percentile
- 100%
- References
- 59
Authors
6- MYMouxing YangCorresponding
Sichuan University, Chengdu University
- YLYunfan Li
Sichuan University, Chengdu University
- PHPeng Hu
Sichuan University, Chengdu University
- JBJinfeng Bai
- JLJiancheng Lv
Sichuan University, Chengdu University
Topics & keywords
- Cluster analysis
- False positive paradox
- Complete information
- Consistency (knowledge bases)
- False positives and false negatives
- Pattern recognition (psychology)
- Robustness (evolution)