The development of information gathering and extraction technology has led to the
popularity of multi-view data, which enables samples to be seen from numerous …

Multimodal fusion focuses on integrating information from multiple modalities with the goal of
more accurate prediction, which has achieved remarkable progress in a wide range of …

Multi-view clustering can partition data samples into their categories by learning a
consensus representation in unsupervised way and has received more and more attention …

In this paper, we study two challenging problems in incomplete multi-view clustering
analysis, namely, i) how to learn an informative and consistent representation among …

Multi-view clustering can explore common semantics from multiple views and has attracted
increasing attention. However, existing works punish multiple objectives in the same feature …

Incomplete multi-view clustering (IMVC) analysis, where some views of multi-view data
usually have missing data, has attracted increasing attention. However, existing IMVC …

View missing and label missing are two challenging problems in the applications of multi-
view multi-label classification scenery. In the past years, many efforts have been made to …

Multi-view clustering has received increasing attention due to its effectiveness in fusing
complementary information without manual annotations. Most previous methods hold the …

The success of existing multi-view clustering relies on the assumption of sample integrity
across multiple views. However, in real-world scenarios, samples of multi-view are partially …

Incomplete multi-view clustering (IMVC) has attracted remarkable attention due to the
emergence of multi-view data with missing views in real applications. Recent methods …