Multi-view contrastive clustering via integrating graph aggregation and confidence enhancement

J Bian, X Xie, JH Lai, F Nie - Information Fusion, 2024 - Elsevier
Multi-view clustering endeavors to effectively uncover consistent clustering patterns across
multiple data sources or feature spaces. This field grapples with two key challenges:(1) the …

Multi-View Representation Learning via View-Aware Modulation

R Wang, H Sun, X Nie, Y Lin, X Xi, Y Yin - Proceedings of the 31st ACM …, 2023 - dl.acm.org
Multi-view (representation) learning derives an entity's representation from its multiple
observable views to facilitate various downstream tasks. The most challenging topic is how …

An Optimal Edge-weighted Graph Semantic Correlation Framework for Multi-view Feature Representation Learning

L Gao, Z Guo, L Guan - ACM Transactions on Multimedia Computing …, 2024 - dl.acm.org
In this article, we present an optimal edge-weighted graph semantic correlation (EWGSC)
framework for multi-view feature representation learning. Different from most existing multi …

Efficient and Effective Multi-View Subspace Clustering for Large-scale Data

Y Lin, H Liu, R Wang, G Chen, C Zhang - arXiv preprint arXiv:2310.09718, 2023 - arxiv.org
Recent multi-view subspace clustering achieves impressive results utilizing deep networks,
where the self-expressive correlation is typically modeled by a fully connected (FC) layer …

Beyond the Known: Ambiguity-Aware Multi-view Learning

Z Fang, S Du, Y Chen, S Wang - ACM Multimedia 2024 - openreview.net
The inherent variability and unpredictability in open multi-view learning scenarios infuse
considerable ambiguity into the learning and decision-making processes of predictors. This …

Task-Oriented Multi-View Representation Learning

R Wang, H Sun, Y Lin, Y Gong, X Nie, Y Yin - openreview.net
Multi-view representation learning aims to learn a high-quality unified representation for an
entity from its multiple observable views to facilitate the performance of downstream tasks. A …