Zhikui Chen

Content-style Disentanglement Guided Representation Learning for Deep Incomplete Multi-view Clustering

Deep incomplete multi-view clustering methods can mine patterns of incomplete multi-view data without labels, gaining great attention in various domains. However, current methods are obsessed with aligning view-specific representations from available samples with complete views to learn view-invariant information for m...

A Semantic Feasibility-Constrained NLI Framework for Scientific Relation Extraction

Scientific relation extraction aims to identify fine-grained semantic relations between domain-specific entities in scientific documents. However, complex scientific expressions, domain adaptation challenges, and subtle relation distinctions make this task difficult. To address these challenges, we propose a novel natu...

Multi-View Fair Clustering Guided by Cross-View Sensitive Information Discrepancy

Multi-view clustering (MVC) aims to uncover latent cluster structures by exploiting complementary information from multiple views. Despite substantial progress in clustering performance, fairness remains an important concern when MVC is applied to socially sensitive scenarios. Recent fair multi-view clustering methods...

Single-phase incomplete multi-view clustering via joint anchor refactoring and tensorial bipartite graph learning.

This work proposes a Single-phase Incomplete Multi-view Clustering via Joint Anchor Refactoring and Tensorial Bipartite Graph (SIMC-ARTB), a novel unified framework that jointly integrates anchor refactoring, aligned graph fusion, and single-phase label learning.

Decomposed tensorized graph learning with representation-induced view recovery for federated incomplete multi-view clustering