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2007
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Locally Competitive Algorithms for Sparse Approximation

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Locally Competitive Algorithms for Sparse Approximation
Practical sparse approximation algorithms (particularly greedy algorithms) suffer two significant drawbacks: they are difficult to implement in hardware, and they are inefficient for time-varying stimuli (e.g., video) because they produce erratic temporal coefficient sequences. We present a class of locally competitive algorithms (LCAs) that correspond to a collection of sparse approximation principles minimizing a weighted combination of reconstruction MSE and a coefficient cost function. These systems use thresholding functions to induce local nonlinear competitions in a dynamical system. Simple analog hardware can implement the required nonlinearities and competitions. We show that our LCAs are stable under normal operating conditions and can produce sparsity levels comparable to existing methods. Additionally, these LCAs can produce coefficients for video sequences that are more regular (i.e., smoother and more predictable) than the coefficients produced by greedy algorithm...
Christopher J. Rozell, Don H. Johnson, Richard G.
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICIP
Authors Christopher J. Rozell, Don H. Johnson, Richard G. Baraniuk, Bruno A. Olshausen
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