AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work proposes a diffusion-driven incomplete multi-view clustering framework based on structured regularization and semantic alignment, termed DISCM, which significantly outperforms existing methods on multiple incomplete multi-view datasets, particularly exhibiting stronger stability and robustness under high missing-rate scenarios.
Multi-view clustering aims to discover shared semantic structures from multiple complementary data views to improve clustering performance. However, most existing methods rely on a single clustering representation for each semantic cluster, which limits the ability to model complex cluster structures and diverse patter...
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.
Multi-view Clustering via Manifold Decomposition is proposed, which directly infers cluster labels from multi-view data without explicit similarity graph construction or anchor selection, and reformulates multi-view clustering as a unified multi-view regression problem, where cluster labels are optimized as model varia...
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.
The Dual-tOpology learning with adapTive Anchors (DOTA) is proposed, which not only learns the sample-anchor relationship but also preserves the topology structure among anchors, significantly enhancing the discriminability of learned representation while preserving the underlying data manifold.
This paper explicitly model and learn correlations between views by utilizing the covariance structure of posterior estimation errors during aggregation, demonstrating the need to go beyond the independence assumption in IMVC.
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