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 …

Abstract Semi-supervised clustering (SSC), a technique integrating semi-supervised
learning and clustering analysis, incorporates the given prior information (eg, class labels …

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 …

In recent years, multi-view multi-label learning has aroused extensive research enthusiasm.
However, multi-view multi-label data in the real world is commonly incomplete due to the …

Non-negative matrix factorization (NMF) has attracted sustaining attention in multi-view
clustering, because of its ability of processing high-dimensional data. In order to learn the …

Multi-view clustering (MVC) has been extensively studied to collect multiple source
information in recent years. One typical type of MVC methods is based on matrix …

Incomplete multi-view clustering which aims to solve the difficult clustering challenge on
incomplete multi-view data collected from diverse domains with missing views has drawn …

Non-negative matrix factorization is widely used in multi-view clustering due to its ability of
learning a common dimension-reduced factor. Recently, it is combined with the label …

Clustering of multi-view data has got broad consideration of the researchers. Multi-view data
is composed through different domain which shows the consistent and complementary …

Most existing clustering methods employ the original multi-view data as input to learn the
similarity matrix which characterizes the underlying cluster structure shared by multiple …