Non-negative Observation-based Decomposition of Operators

Manuel Lopez Radcenco 1, 2 Ronan Fablet 2, 1 Abdeldjalil Aissa El Bey 2, 3
1 Lab-STICC_IMTA_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Lab-STICC_IMTA_CACS_COM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : The problem of observation-based characterization of operators, closely related to the well-studied problem of blind source separation, remains nonetheless considerably less studied. Inspired by the recent success of non-negative and sparse blind source separation, we aim at extending constrained blind source separation models to the data-driven characterization of operators. We introduce a novel non-negative decomposition model for linear operators and investigate different parameter estimation algorithms. We study and compare the proposed algorithms in terms of identification and reconstruction performance in a variety of experimental settings, in order to gain insight into the robustness and limitations of the proposed algorithms. We further discuss the main contribution of our approach compared with state-of-the-art methods for the analysis and decomposition of operators.
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Submitted on : Tuesday, October 9, 2018 - 7:08:39 PM
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Manuel Lopez Radcenco, Ronan Fablet, Abdeldjalil Aissa El Bey. Non-negative Observation-based Decomposition of Operators. 2018. ⟨hal-01891692⟩

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