An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem

Given a large overcomplete dictionary of basis vectors, the goal is to simultaneously represent L>1 signal vectors using coefficient expansions marked by a common sparsity profile. This generalizes the standard sparse representation problem to the case where multiple responses exist that were put...

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Veröffentlicht in:	IEEE transactions on signal processing 2007-07, Vol.55 (7), p.3704-3716
Hauptverfasser:	Wipf, D.P., Rao, B.D.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithm design and analysis Algorithms Applied sciences Approximation Automatic relevance determination Bayesian analysis Bayesian methods Cost function Design for experiments Detection, estimation, filtering, equalization, prediction Dictionaries Empirical analysis empirical Bayes Exact sciences and technology Focusing Image processing Information, signal and communications theory Input variables Matching pursuit algorithms Mathematical analysis multiple response models Pursuit algorithms Signal and communications theory Signal generators Signal processing Signal, noise simultaneous sparse approximation sparse Bayesian learning Sparsity Studies Telecommunications and information theory Thermal expansion variable selection Vectors (mathematics)
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