Convergence and Rate Analysis of Neural Networks for Sparse Approximation

We present an analysis of the Locally Competitive Algorithm (LCA), which is a Hopfield-style neural network that efficiently solves sparse approximation problems (e.g., approximating a vector from a dictionary using just a few nonzero coefficients). This class of problems plays a significant role in...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 23; H. 9; S. 1377 - 1389
Hauptverfasser:	Balavoine, A., Romberg, J., Rozell, C. J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.09.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial intelligence Computer science; control theory; systems Computer Simulation Connectionism. Neural networks Convergence Dictionaries Exact sciences and technology Exponential convergence global stability Least squares approximation locally competitive algorithm Lyapunov function Lyapunov methods Models, Statistical Neural networks Neural Networks (Computer) nonsmooth objective Optimization Pattern Recognition, Automated - methods Sample Size sparse approximation Studies Theoretical computing Hopfield neural nets Exponential convergence Dictionaries Competitiveness Global optimum global stability locally competitive algorithm Network management Neural network Function approximation Competitive algorithms Convergence speed nonsmooth objective, sparse approximation Convergence rate Signal processing Sparse representation Objective function Numerical convergence Hopfield model Lyapunov function
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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