Current Multiview Graph Clustering (MGC) methods primarily focus on fusing multiple graphs and/or spectral embeddings to seek common clustering sets, however, the tendency between nodes and the locality between clusters and views are scarcely considered. Specifically, there are consistent topological structures in mult...
Danyang Wu, Peng-Lei Wang, Jun-Jie Liang et al.· IEEE Transactions on Pattern...· 0 citations
In most existing multi-view clustering scenarios, samples of the same object across views are typically assumed to be strictly aligned, forming a key prerequisite for cross-view consistency. In practice, however, view-wise data acquisition and processing are often independent, making the View-unaligned Problem (VuP) ub...
Zhe Cao, Tao Zhang, Yi-Chen Wang et al.· IEEE Transactions on Pattern...· 0 citations
This article utilizes bias-variance-diversity (BVD) decomposition theory to examine the impact of noisy labels on three mainstream ensemble paradigms: Bagging, Boosting, and Stacking and characterizes the mechanisms behind performance degradation.
Guanxiong He, Jie Wang, Zhiyong Li et al.· IEEE Transactions on Neural...· 1 citation
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