6 accepted papers
In incomplete multi-view clustering, unbalanced missingness is prevalent, where different views exhibit significantly varying missing rates, causing severe observation bias. This imbalance poses two core challenges: models develop serious learning biases by over-relying on low-missing-rate views whi…
Multi-view clustering aims to uncover shared semantics and complementary information across different views. However, the inherent heterogeneity among views poses significant challenges to effective collaborative modeling and information integration. While recent studies have introduced distillation
In real-world applications, multi-view data often suffer from missing situations due to privacy protection and sensor failures. Such incomplete scenarios not only reduce information availability but also cause significant imbalance among views: certain "strong views" dominate the fusion process, whi
Compressing long chains of thought (CoT) into compact latent tokens is crucial for efficient reasoning with large language models (LLMs). Recent studies employ autoencoders to achieve this by reconstructing textual CoT from latent tokens, thus encoding CoT semantics. However, treating textual CoT as…
Hyperspectral image (HSI) clustering facilitates the unsupervised discrimination of complex surface materials but traditionally relies on the idealized assumption of centralized data availability. In real-world scenarios, however, this assumption clashes with data privacy regulations and the physica
Deep graph clustering (DGC), which aims to unsupervisedly separate the nodes in an attribute graph into different clusters, has seen substantial potential in various industrial scenarios like community detection and recommendation. However, the real-world attribute graphs, e.g., social networks inte…