Abstract. In this paper we elaborate on a kernel extension to tensorbased data analysis. The proposed ideas find applications in supervised learning problems where input data have ...
Marco Signoretto, Lieven De Lathauwer, Johan A. K....
Abstract. We discuss causal structure learning based on linear structural equation models. Conventional learning methods most often assume Gaussianity and create many indistinguish...
The self-organizing map SOM is widely used as a data visualization method in various engineering applications. It performs a non-linear mapping from a high-dimensional data spac...
Analysis of causal effects between continuous-valued variables typically uses either autoregressive models or structural equation models with instantaneous effects. Estimation of ...
Abstract. We introduce a new data structure, called MiGaL for “Multiple Graph Layers”, that is composed of various graphs linked together ions of abstraction/refinement. The n...