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.
Mots clés
non-negativity
adaptive filtering
Algorithm design and analysis
Vectors
hyperspectral data processing
mean weight-error behavior analysis
Cost function
Mathematical model
Hyperspectral imaging
Equations
sparsity
gradient methods
identification
online system identification
L1-norm constraints algorithm
information processing
nonnegativity constraints
Convergence
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