Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …

Cluster analyzes have been widely used in mental health research to decompose inter-
individual heterogeneity by identifying more homogeneous subgroups of individuals …

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

Aligning distributions of view representations is a core component of today's state of the art
models for deep multi-view clustering. However, we identify several drawbacks with naively …

Multi-view clustering (MVC) has attracted more and more attention in the recent few years by
making full use of complementary and consensus information between multiple views to …

In the big data era, the data are generated from different sources or observed from different
views. These data are referred to as multi-view data. Unleashing the power of knowledge in …

Incomplete multi-view clustering (IMVC) optimally combines multiple pre-specified
incomplete views to improve clustering performance. Among various excellent solutions, the …

Incomplete multi-view clustering, which aims to solve the clustering problem on the
incomplete multi-view data with partial view missing, has received more and more attention …

Incomplete multi-view clustering optimally integrates a group of pre-specified incomplete
views to improve clustering performance. Among various excellent solutions, multiple kernel …

Multiple kernel clustering (MKC) algorithms optimally combine a group of pre-specified base
kernel matrices to improve clustering performance. However, existing MKC algorithms …