On-line nonlinear sparse approximation of functions
Résumé
This paper provides new insights into on-line nonlinear sparse approximation of functions based on the coherence criterion. We revisit previous work, and propose tighter bounds on the approximation error based on the coherence criterion. Moreover, we study the connections between the coherence criterion and both the approximate linear dependence criterion and the principal component analysis. Finally, we derive a kernel normalized LMS algorithm based on the coherence criterion, which has linear computational complexity on the model order. Initial experimental results are presented on the performance of the algorithm.
Mots clés
machine learning
sparsity
adaptive filtering
approximation theory
function approximation
least mean squares methods
online nonlinear sparse approximation
coherence criterion
approximate linear dependence criterion
kernel normalized LMS algorithm
linear computational complexity
model order
least mean squares method
Kernel
Coherence
Least squares approximation
Computational complexity
Filtering algorithms
Training data
Approximation error
Linear approximation
Principal component analysis
Dictionaries
| Origine | Fichiers produits par l'(les) auteur(s) |
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