Abstract The Artificial Intelligence Internet of Things (AIoT) is an emerging concept aiming to
perceive, understand and connect the 'intelligent things' to make the intercommunication of …

Multiview data clustering attracts more attention than their single-view counterparts due to
the fact that leveraging multiple independent and complementary information from multiview …

Subspace clustering is an important unsupervised clustering approach. It is based on the
assumption that the high-dimensional data points are approximately distributed around …

The ubiquitous information from multiple-view data, as well as the complementary
information among different views, is usually beneficial for various tasks, for example …

Preserving global and local structures during projection learning is very important for feature
extraction. Although various methods have been proposed for this goal, they commonly …

The density peak clustering algorithm treats local density peaks as cluster centers, and
groups non-center data points by assuming that one data point and its nearest higher …

Subspace techniques have gained much attention for their remarkable efficiency in
representing high-dimensional data, in which sparse subspace clustering (SSC) and low …

Multi-view clustering is a learning paradigm based on multi-view data. Since statistic
properties of different views are diverse, even incompatible, few approaches implement …

Significant attention to multiple kernel graph-based clustering (MKGC) has emerged in
recent years, primarily due to the superiority of multiple kernel learning (MKL) and the …

A single latent factor (LF)-dependent, nonnegative, and multiplicative update (SLF-NMU)
learning algorithm is highly efficient in building a nonnegative LF (NLF) model defined on a …