Name
Abstract | ||
|---|---|---|
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 independent component analysis using diffusion kernels and spectral analysis. Employment of the proposed algorithm on both synthetic and practical examples has shown accurate recovery of parameters. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TSP.2011.2177973 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
independent component analysis,learning (artificial intelligence),signal processing,black box,diffusion Kernels,generic data-driven method,independent controlling parameters,linear systems parametrization,multidimensional signal processing,nonlinear independent component analysis,supervised algorithm,supervised learning,Kernel,linear systems,modeling,multidimensional signal processing,non-parametric estimation,nonlinear dynamical systems,system identification | Kernel (linear algebra),Black box (phreaking),Signal processing,Multidimensional signal processing,Mathematical optimization,Parametrization,Linear system,Computer science,Independent component analysis,System identification | Journal |
Volume | Issue | ISSN |
60 | 3 | 1053-587X |
Citations | PageRank | References |
16 | 0.98 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Talmon, R. | 1 | 16 | 0.98 |
| Kushnir, D. | 2 | 16 | 0.98 |
| Coifman, R.R. | 3 | 939 | 201.41 |
| Irun R. Cohen | 4 | 296 | 21.62 |