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» A Practical Method for the Sparse Resultant
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JMLR
2012
13 years 8 days ago
Universal Measurement Bounds for Structured Sparse Signal Recovery
Standard compressive sensing results state that to exactly recover an s sparse signal in Rp , one requires O(s · log p) measurements. While this bound is extremely useful in prac...
Nikhil S. Rao, Ben Recht, Robert D. Nowak
ICPR
2008
IEEE
15 years 11 months ago
Fast protein homology and fold detection with sparse spatial sample kernels
In this work we present a new string similarity feature, the sparse spatial sample (SSS). An SSS is a set of short substrings at specific spatial displacements contained in the or...
Pai-Hsi Huang, Pavel P. Kuksa, Vladimir Pavlovic
SIAMNUM
2010
120views more  SIAMNUM 2010»
14 years 4 months ago
Sparse Spectral Approximations of High-Dimensional Problems Based on Hyperbolic Cross
Hyperbolic cross approximations by some classical orthogonal polynomials/functions in both bounded and unbounded domains are considered in this paper. Optimal error estimates in pr...
Jie Shen, Li-lian Wang
CORR
2010
Springer
208views Education» more  CORR 2010»
14 years 6 months ago
Real-time Robust Principal Components' Pursuit
In the recent work of Candes et al, the problem of recovering low rank matrix corrupted by i.i.d. sparse outliers is studied and a very elegant solution, principal component pursui...
Chenlu Qiu, Namrata Vaswani
ICIP
2007
IEEE
15 years 4 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. ...