The Internet of Things (IoT) is enabled by the latest developments in smart sensors,
communication technologies, and Internet protocols with broad applications. Collecting data …

Multi-view graph-based clustering aims to provide clustering solutions to multi-view data.
However, most existing methods do not give sufficient consideration to weights of different …

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 …

An important underlying assumption that guides the success of the existing multiview
learning algorithms is the full observation of the multiview data. However, such rigorous …

This paper studies clustering of multi-view data, known as multi-view clustering. Among
existing multi-view clustering methods, one representative category of methods is the graph …

The missing modality issue is critical but non-trivial to be solved by multi-modal models.
Current methods aiming to handle the missing modality problem in multi-modal tasks, either …

Incomplete multi-view clustering (IMC) aims to integrate the complementary information from
incomplete views to improve clustering performance. Most existing IMC methods try to fill the …

Incomplete multi-view clustering partitions multi-view data suffering from missing views, for
which matrix factorization approaches seek the latent representation of incomplete multi …

We propose a mechanism to use data with missing values for designing classifiers which is
different from predicting missing values for classification. Our imputation method uses an …

Beyond existing multi-view clustering, this paper studies a more realistic clustering scenario,
referred to as incomplete multi-view clustering, where a number of data instances are …