The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems (d3 as) that efficiently integrate the observationa...
Adrian Sandu, Emil M. Constantinescu, Wenyuan Liao...
A model-constrained adaptive sampling methodology is proposed for reduction of large-scale systems with high-dimensional parametric input spaces. Our model reduction method uses a ...
The Dirichlet Process Mixture (DPM) models represent an attractive approach to modeling latent distributions parametrically. In DPM models the Dirichlet process (DP) is applied es...
Asma Rabaoui, Nicolas Viandier, Juliette Marais, E...
It is a challenging task to accurately model the performance of a face recognition system, and to predict its individual recognition results under various environments. This paper...
Analysis of video data usually requires training classifiers in high dimensional feature spaces. This paper proposes a layered Gaussian mixture model (LGMM) to exploit high dimens...