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2012

Parametrization of Linear Systems Using Diffusion Kernels

10 years 26 days ago
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...
Ronen Talmon, Dan Kushnir, Ronald R. Coifman, Isra
Added 25 Apr 2012
Updated 25 Apr 2012
Type Journal
Year 2012
Where TSP
Authors Ronen Talmon, Dan Kushnir, Ronald R. Coifman, Israel Cohen, Sharon Gannot
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