Although multi-view learning has made significant progress over the past few decades, it is
still challenging due to the difficulty in modeling complex correlations among different views …

Despite multi-view learning progressed fast in past decades, it is still challenging due to the
difficulty in modeling complex correlation among different views, especially under the …

In many real-world applications we deal with multiple related classification/regression/
clustering tasks. For example, in the prediction of therapy outcome (Bickel et al., 2008), the …

Clustering on multi-view data has attracted much more attention in the past decades. Most
previous studies assume that each instance appears in all views, or there is at least one …

Multimodal learning aims at utilizing information from a variety of data modalities to improve
the generalization performance. One common approach is to seek the common information …

Learning from multi-view multi-label data has wide applications. Two main challenges
characterize this learning task: incomplete views and missing (weak) labels. The former …

Understanding the progression of chronic diseases can empower the sufferers in taking
proactive care. To predict the disease status in the future time points, various machine …

Effective fusion of structural magnetic resonance imaging (sMRI) and functional magnetic
resonance imaging (fMRI) data has the potential to boost the accuracy of infant age …

With the proliferation of social network services, more and more social users, such as
individuals and organizations, are simultaneously involved in multiple social networks for …

Motivation Cancer subtype diagnosis is crucial for its precise treatment and different
subtypes need different therapies. Although the diagnosis can be greatly improved by fusing …