Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

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 …

With the explosive growth of information technology, multi-view graph data have become
increasingly prevalent and valuable. Most existing multi-view clustering techniques either …

In this article, we propose a unified framework to solve the following two challenging
problems in incomplete multi-view representation learning: i) how to learn a consistent …

Existing multi-view classification algorithms focus on promoting accuracy by exploiting
different views, typically integrating them into common representations for follow-up tasks …

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

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 …

In this paper, we study an untouched problem in visible-infrared person re-identification (VI-
ReID), namely, Twin Noise Labels (TNL) which refers to as noisy annotation and …

Cross-modal matching, which aims to establish the correspondence between two different
modalities, is fundamental to a variety of tasks such as cross-modal retrieval and vision-and …

In real-world applications, it is common that only a portion of data is aligned across views
due to spatial, temporal, or spatiotemporal asynchronism, thus leading to the so-called …