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» Phase Transitions for Greedy Sparse Approximation Algorithms
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IJCNN
2006
IEEE
13 years 10 months ago
Greedy forward selection algorithms to Sparse Gaussian Process Regression
Abstract— This paper considers the basis vector selection issue invloved in forward selection algorithms to sparse Gaussian Process Regression (GPR). Firstly, we re-examine a pre...
Ping Sun, Xin Yao
CISS
2010
IEEE
12 years 8 months ago
Turbo reconstruction of structured sparse signals
—This paper considers the reconstruction of structured-sparse signals from noisy linear observations. In particular, the support of the signal coefficients is parameterized by h...
Philip Schniter
ICIP
2007
IEEE
13 years 11 months ago
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 inefficie...
Christopher J. Rozell, Don H. Johnson, Richard G. ...
SAT
2005
Springer
104views Hardware» more  SAT 2005»
13 years 10 months ago
Observed Lower Bounds for Random 3-SAT Phase Transition Density Using Linear Programming
We introduce two incomplete polynomial time algorithms to solve satisfiability problems which both use Linear Programming (LP) techniques. First, the FlipFlop LP attempts to simul...
Marijn Heule, Hans van Maaren
TSP
2008
124views more  TSP 2008»
13 years 4 months ago
Dictionary Preconditioning for Greedy Algorithms
This article introduces the concept of sensing dictionaries. It presents an alteration of greedy algorithms like thresholding or (Orthogonal) Matching Pursuit which improves their...
Karin Schnass, Pierre Vandergheynst