Independent component analysis (ICA) is a data analysis tool that can be seen as a
refinement of principal component analysis or factor analysis. ICA recovers the structures in …

Second‐order source separation (SOS) is a data analysis tool which can be used for
revealing hidden structures in multivariate time series data or as a tool for dimension …

Clustering is a fundamental primitive in unsupervised learning which gives rise to a rich
class of computationally-challenging inference tasks. In this work, we focus on the canonical …

Increasing energy efficiency is often considered to be one of the main ways of reducing
greenhouse gas emissions. However, efficiency gains that reduce the cost of energy …

The median and median target estimates in sign-restricted SVARs are driven by a highly
informative prior for the set-identified structural parameters. This paper proposes an …

Many linear dimension reduction methods proposed in the literature can be formulated
using an appropriate pair of scatter matrices. The eigen-decomposition of one scatter matrix …

Scatter matrices generalize the covariance matrix and are useful in many multivariate data
analysis methods, including well-known principal component analysis (PCA), which is …

In high reliability standards fields such as automotive, avionics or aerospace, the detection
of anomalies is crucial. An efficient methodology for automatically detecting multivariate …

Several recently developed identification techniques for structural VAR models are based on
the assumption of non-Gaussianity. So-called independence based identification provides …

In preprocessing tensor-valued data, eg, images and videos, a common procedure is to
vectorize the observations and subject the resulting vectors to one of the many methods …