During the last years, low‐rank tensor approximation has been established as a new tool in
scientific computing to address large‐scale linear and multilinear algebra problems, which …

Data-driven solutions are playing an increasingly important role in numerous practical
problems across multiple disciplines. The shift from the traditional model-driven approaches …

Tensors and tensor decompositions are very powerful and versatile tools that can model a
wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which …

The problem of incomplete data–ie, data with missing or unknown values–in multi-way
arrays is ubiquitous in biomedical signal processing, network traffic analysis, bibliometrics …

Learning filters to produce sparse image representations in terms of overcomplete
dictionaries has emerged as a powerful way to create image features for many different …

The data in many disciplines such as social networks, Web analysis, etc. is link-based, and
the link structure can be exploited for many different data mining tasks. In this article, we …

Tensor decomposition is a fundamental unsupervised machine learning method in data
science, with applications including network analysis and sensor data processing. This work …

Joint analysis of data from multiple sources has the potential to improve our understanding
of the underlying structures in complex data sets. For instance, in restaurant …

Recently, deep learning has demonstrated great success in computer vision with the
capability to learn powerful image features from a large training set. However, most of the …

The widespread use of multisensor technology and the emergence of big data sets have
brought the necessity to develop more versatile tools to represent higher order data with …