AI RESEARCH
Robust Multi-view Clustering against Imperfect Information
arXiv CS.CV
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ArXi:2606.04343v1 Announce Type: new Real-world multi-view data always suffer from imperfect information problem, where the view-specific observations are absent (i.e., Incomplete Views, IV) and cross-view correspondences are mismatched (i.e., Noisy Correspondences, NC) for certain instances. As a remedy, numerous IV- and NC-oriented multi-view clustering (MvC) methods have been proposed, which. however. require either reliable correspondences or sufficiently complete instances, thus stopping short of addressing the imperfect information problem.