4 accepted papers
In multi-view clustering (MVC), complementary and consistent information from multiple views is integrated to improve clustering performance. However, inter-view sample correspondences may be partially missing in practice, making it difficult to learn cross-view consistency, which leads to the parti
Multimodal data is typically collected through heterogeneous sensors and processing pipelines. However, due to variations in acquisition environments, device capabilities, and feature extraction methods, such data often suffers from incompleteness and inconsistent quality across modalities. To addre
Multi-modal representation is faithful and highly effective in describing real-world data samples' characteristics by describing their complementary information. However, the collected data often exhibits incomplete and misaligned characteristics due to factors such as inconsistent sensor frequencie
Over the last decade, many multi-view clustering (MVC) methods have achieved promising results with intact and completely correct correspondence of multi-view data, which is hard to satisfy in practice leading to the problem of partially view-aligned clustering. In this paper, we propose a novel met…