TL;DR
This work proposes a Self-supervised Probability Distribution-driven Deep Incomplete Multi-view Clustering method (SPDIM), which first handles missing views at the data level, utilizing uncertainty-aware sampling to interpolate missing information and thereby enforce global consistency across samples.
IASR is proposed, an Instance-Aligned Semantic Reconstruction framework for IMVC that formulates missing-view recovery as a cross-view token alignment generator, and introduces a stable–active dual-encoder representation architecture with a generative-adaptive contrastive learning strategy that tightly couples view com...
This work proposes a novel deep constrained multi-view clustering framework, namely SeSCE, which encodes view embeddings into a spherical space, leveraging pairwise constraints to maximize intra-class compactness and inter-class separability and introduces a confidence-aware pseudo-constraint mining mechanism.
This paper proposes salient-residual decoupled multi-view learning for clustering, SRDMVC, introducing a novel decomposition-fusion iterative optimization, which separates the feature space into a salient space and a residual subspace effectively and fuses them using a novel attention mechanism.
A content-style disentanglement guided representation learning is proposed for the incomplete multi-view clustering (CSMVC), which designs a view-specific dual-representation learning architecture that extracts view-invariant content representations and view-specific style representations from self-supervised reconstru...
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