Abstract Model-based clustering is a popular approach for clustering multivariate data which
has seen applications in numerous fields. Nowadays, high-dimensional data are more and …

Graphs representing complex systems often share a partial underlying structure across
domains while retaining individual features. Thus, identifying common structures can shed …

Here the problem of selecting the number of clusters in cluster analysis is considered.
Recently, the concept of clustering stability, which measures the robustness of any given …

We propose a novel sparse tensor decomposition method, namely the tensor truncated
power method, that incorporates variable selection in the estimation of decomposition …

Dynamic tensor data are becoming prevalent in numerous applications. Existing tensor
clustering methods either fail to account for the dynamic nature of the data, or are …

In context to high-dimensional clustering, the concept of feature weighting has gained
considerable importance over the years to capture the relative degrees of importance of …

We consider joint estimation of multiple graphical models arising from heterogeneous and
high-dimensional observations. Unlike most previous approaches which assume that the …

Consistency plays an important role in learning theory. However, in multiple kernel
clustering (MKC), the consistency of kernel weights has not been sufficiently investigated. In …

Penalized regression models are popularly used in high-dimensional data analysis to
conduct variable selection and model fitting simultaneously. Whereas success has been …

This article addresses the challenge of determining optimal product family architectures with
customer preference data. The proposed model, predictive data-driven product family …