6 accepted papers
One of the main approaches to system identification of networks of time series or signals is conditional independence graphical (CIG) modeling. In the Gaussian case, the conditional dependence structure of the nodal time series is determined by the location of zeros in the precision matrix (inverse…
In many engineering applications the state of a dynamical system is modelled by a Stochastic Differential Equation (SDE) evolving in a “curved” (non-Euclidean) space such as the Stiefel manifold - the set of n × p real matrices with orthonormal columns, (n ≥ p). Due to the advances in computing powe…
In many important engineering applications the state dynamics of a system are modelled by Stochastic Differential Equations (SDEs) evolving in non-Euclidean spaces such as matrix Lie groups. Due to the advances in computing power, the problem of state estimation can be efficiently addressed by the p…
There has been significant interest in sparse inverse covariance estimation in areas such as statistics, machine learning, and signal processing. In this problem, the sparse inverse of a covariance matrix of a multivariate normal distribution is estimated. A Penalised Log-Likelihood (PLL) optimisati…
Significant attention has been given to minimizing a penalized least squares criterion for estimating sparse solutions to large linear systems of equations. The penalty induces sparsity and the natural choice is the so-called l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://…
High dimensional data is often modeled as a linear combination of a sparse component, a low-rank component, and noise. An example is a video sequence of a busy scene where the background is the low-rank part and the foreground, e.g. moving pedestrians, is the sparse part. Sparse and low rank (SLR) m…