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» Sparse Recovery Using Sparse Random Matrices
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CRV
2011
IEEE
305views Robotics» more  CRV 2011»
13 years 9 months ago
Motion Segmentation by Learning Homography Matrices from Motor Signals
—Motion information is an important cue for a robot to separate foreground moving objects from the static background world. Based on the observation that the motion of the backgr...
Changhai Xu, Jingen Liu, Benjamin Kuipers
JMLR
2010
136views more  JMLR 2010»
14 years 4 months ago
High Dimensional Inverse Covariance Matrix Estimation via Linear Programming
This paper considers the problem of estimating a high dimensional inverse covariance matrix that can be well approximated by "sparse" matrices. Taking advantage of the c...
Ming Yuan
ICASSP
2009
IEEE
15 years 4 months ago
Compressive spectral estimation for nonstationary random processes
We propose a “compressive” estimator of the Wigner-Ville spectrum (WVS) for time-frequency sparse, underspread, nonstationary random processes. A novel WVS estimator involving...
Alexander Jung, Georg Tauböck, Franz Hlawatsc...
ICASSP
2008
IEEE
15 years 4 months ago
Finding needles in noisy haystacks
The theory of compressed sensing shows that samples in the form of random projections are optimal for recovering sparse signals in high-dimensional spaces (i.e., finding needles ...
Rui M. Castro, Jarvis Haupt, Robert Nowak, Gil M. ...
SIAMNUM
2011
102views more  SIAMNUM 2011»
14 years 21 days ago
Differential Equations for Roaming Pseudospectra: Paths to Extremal Points and Boundary Tracking
Abstract. When studying the ε-pseudospectrum of a matrix, one is often interested in computing the extremal points having maximum real part or modulus. This is a crucial step, for...
Nicola Guglielmi, Christian Lubich