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 …

In several fields, deep learning has achieved tremendous success. Multi-view learning is a
workable method for handling data from several sources. For clustering multi-view data …

Multi-view clustering has become a rapidly growing field in machine learning and data
mining areas by combining useful information from different views for last decades. Although …

With the proliferation of multimedia applications, data is frequently derived from multiple
sources, leading to the accelerated advancement of multi-view clustering (MVC) methods. In …

Although multi-view clustering (MVC) has achieved remarkable performance by integrating
the complementary information of views, it is inefficient when facing scalable data …

The accurate mesoscale eddy identification methods with deep learning framework depend
on either single eddy characteristic from altimeter missions or multi-step eddy examination …

Multi-view subspace clustering achieves impressive performance for high-dimensional data.
However, many of these models do not sufficiently mine the intrinsic information among …

Recently, researchers have focused on utilizing given heterogeneous features to explore
obvious discrimination information for clustering. Most of the current work exploits …

Multi-view clustering aims to group objects with high similarity into one group according to
the heterogeneous features of different views. The graph-based clustering methods have …

Recently, many multi-view clustering (MVC) methods based on graphs have been proposed
to address prevalent multi-view data. For these methods, the multi-graph fusion step, aim of …