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Communication Dans Un Congrès Année : 2011

Online system identification under non-negativity and l₁-norm constraints algorithm and weight behavior analysis

Résumé

Information processing with L1-norm constraint has been a topic of considerable interest during the last five years since it produces sparse solutions. Non-negativity constraints are also desired properties that can usually be imposed due to inherent physical characteristics of real-life phenomena. In this paper, we investigate an online method for system identification subject to these two families of constraints. Our approach differs from existing techniques such as projected-gradient algorithms in that it does not require any extra projection onto the feasible region. The mean weight-error behavior is analyzed analytically. Experimental results show the advantage of our approach over some existing algorithms. Finally, an application to hyperspectral data processing is considered.
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Dates et versions

hal-01966026 , version 1 (27-12-2018)

Identifiants

  • HAL Id : hal-01966026 , version 1

Citer

Jie Chen, Cédric Richard, Henri Lantéri, Céline Theys, Paul Honeine. Online system identification under non-negativity and l₁-norm constraints algorithm and weight behavior analysis. Proc. 19th European Conference on Signal Processing (EUSIPCO), 2011, Barcelona, Spain. pp.1919-1923. ⟨hal-01966026⟩
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