Parametrization of Linear Systems Using Diffusion Kernels

—Modeling natural and artificial systems has played a key role in various applications and has long been a task that has drawn enormous efforts. In this work, instead of exploring predefined models, we aim to identify implicitly the system degrees of freedom. This approach circumvents the dependency of a specific predefined model for a specific task or system and enables a generic data-driven method to characterize a system based solely on its output observations. We claim that each system can be viewed as a black box controlled by several independent parameters. Moreover, we assume that the perceptual characterization of the system output is determined by these independent parameters. Consequently, by recovering the independent controlling parameters, we find in fact a generic model for the system. In this work, we propose a supervised algorithm to recover the controlling parameters of natural and artificial linear systems. The proposed algorithm relies on nonlinear independe...

• Type: Journal
• Year: 2012
• Where: TSP
• Authors: Ronen Talmon, Dan Kushnir, Ronald R. Coifman, Israel Cohen, Sharon Gannot
• Keywords: Accurate Recovery, Artificial Intelligence, Independent Component Analysis, Independent Parameters, TSP 2012