AI RESEARCH
ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering
arXiv CS.LG
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ArXi:2011.10331v4 Announce Type: replace-cross Multi-view clustering has wide applications in many image processing scenarios. In these scenarios, original image data often contain missing instances and noises, which is ignored by most multi-view clustering methods. However, missing instances may make these methods difficult to use directly and noises will lead to unreliable clustering results. In this paper, we propose a novel Auto-weighted Noisy and Incomplete Multi-view Clustering framework (ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression model.