Prior-oriented Anchor Learning with Coalesced Semantics for Multi-View Clustering

摘要

Anchor-based multi-view clustering has received a lot of attention due to its efficiency in handling large-scale datasets. However, existing methods rely on penalty-based regularization terms in anchor graphs to handle noise and outliers but overlook the role of consistent semantics in label contributions, failing to effectively mitigate their impact and potentially deviating from actual data distributions. In addition, most strategies use adaptive anchor learning without considering the veracity of anchor selection and the lack of sufficient semantic support in modeling semantic consistency, which leads to anchors deviating from the clustering center. To solve the above problems, we propose a novel method called Prior-oriented Anchor Learning with Coalesced Semantics for Multi-View Clustering (PALCS). Specifically, PALCS strips out inconsistent semantics from anchor graphs to be processed separately through coalesced semantics and highlights consistent semantics to reveal the underlying shared structure of the data. Moreover, PALCS enhances the semantic consistency and discriminative properties of anchors by directing them to be evenly distributed across clusters via the prior matrix. Finally, the clustering labels are directly obtained by non-negative matrix decomposition, avoiding additional post-processing steps. Extensive experimental evidence demonstrates the superiority of our method compared to state-of-the-art methods.