In this paper, we propose a novel sparse source separation method that can be applied even if the number of sources is unknown. Recently, many sparse source separation approaches ...
Graphical models are useful for capturing interdependencies of statistical variables in various fields. Estimating parameters describing sparse graphical models of stationary mul...
We describe the parallelization of an efficient algorithm for balanced truncation that allows to reduce models with state-space dimension up to O(105 ). The major computational tas...
A collaborative framework for detecting the different sources in mixed signals is presented in this paper. The approach is based on CHiLasso, a convex collaborative hierarchical s...
We develop an approach for a sparse representation for Gaussian Process (GP) models in order to overcome the limitations of GPs caused by large data sets. The method is based on a...