Pages that link to "Item:Q3549018"

The following pages link to Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit (Q3549018):

Displaying 50 items.

• DOA estimation with a rotational uniform linear array (RULA) and unknown spatial noise covariance (Q784552) (← links)
• Compressive sensing-based wind speed estimation for low-altitude wind-shear with airborne phased array radar (Q784564) (← links)
• Iteratively weighted thresholding homotopy method for the sparse solution of underdetermined linear equations (Q829377) (← links)
• RBF-network based sparse signal recovery algorithm for compressed sensing reconstruction (Q889379) (← links)
• Adaptive projected gradient thresholding methods for constrained \(l_0\) problems (Q892792) (← links)
• Comparison of parametric sparse recovery methods for ISAR image formation (Q893680) (← links)
• Kernel-based sparse representation for gesture recognition (Q898216) (← links)
• A classification-oriented dictionary learning model: explicitly learning the particularity and commonality across categories (Q898347) (← links)
• From compression to compressed sensing (Q905909) (← links)
• Sparsity in time-frequency representations (Q967573) (← links)
• Sparse approximate solution of partial differential equations (Q972312) (← links)
• Combinatorial sublinear-time Fourier algorithms (Q972615) (← links)
• Chirp sensing codes: Deterministic compressed sensing measurements for fast recovery (Q1006638) (← links)
• CoSaMP: Iterative signal recovery from incomplete and inaccurate samples (Q1012549) (← links)
• A swapping-based refinement of orthogonal matching pursuit strategies (Q1027247) (← links)
• Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit (Q1029209) (← links)
• Random sampling of sparse trigonometric polynomials. II: Orthogonal matching pursuit versus basis pursuit (Q1029548) (← links)
• Compressive sensing for subsurface imaging using ground penetrating radar (Q1032423) (← links)
• On a simple derivation of the complementary matching pursuit (Q1048880) (← links)
• LOL selection in high dimension (Q1621355) (← links)
• MOEA/D with chain-based random local search for sparse optimization (Q1626237) (← links)
• Surface inpainting with sparsity constraints (Q1632393) (← links)
• Instrumental variable-based OMP identification algorithm for Hammerstein systems (Q1654317) (← links)
• A new sensor selection scheme for Bayesian learning based sparse signal recovery in WSNs (Q1661457) (← links)
• Prior model identification during subsurface flow data integration with adaptive sparse representation techniques (Q1663631) (← links)
• MIMO radar imaging based on smoothed \(l_0\) norm (Q1666726) (← links)
• Backtracking-based simultaneous orthogonal matching pursuit for sparse unmixing of hyperspectral data (Q1666730) (← links)
• PROMP: a sparse recovery approach to lattice-valued signals (Q1669069) (← links)
• Recovery of block sparse signals under the conditions on block RIC and ROC by BOMP and BOMMP (Q1673807) (← links)
• Iterative nearest neighbors (Q1677025) (← links)
• Relaxed sparse eigenvalue conditions for sparse estimation via non-convex regularized regression (Q1677029) (← links)
• Sparsity enabled cluster reduced-order models for control (Q1683852) (← links)
• Exact recovery of sparse multiple measurement vectors by \(l_{2,p}\)-minimization (Q1691327) (← links)
• A sharp recovery condition for block sparse signals by block orthogonal multi-matching pursuit (Q1700711) (← links)
• Model reduction method using variable-separation for stochastic saddle point problems (Q1700715) (← links)
• A two-level method for sparse time-frequency representation of multiscale data (Q1703872) (← links)
• Accelerating near-field 3D imaging approach for joint high-resolution imaging and phase error correction (Q1710937) (← links)
• Sparsity and incoherence in orthogonal matching pursuit (Q1710949) (← links)
• Optimization methods for regularization-based ill-posed problems: a survey and a multi-objective framework (Q1712546) (← links)
• A computational study of the role of spatial receptive field structure in processing natural and non-natural scenes (Q1714199) (← links)
• Roles of clustering coefficient for the network reconstruction (Q1721001) (← links)
• Efficient projected gradient methods for cardinality constrained optimization (Q1729947) (← links)
• Minimization of transformed \(L_1\) penalty: theory, difference of convex function algorithm, and robust application in compressed sensing (Q1749455) (← links)
• Adaptive compressive learning for prediction of protein-protein interactions from primary sequence (Q1783649) (← links)
• Recovery of seismic wavefields by an \(l_{q}\)-norm constrained regularization method (Q1785033) (← links)
• Capped \(\ell_p\) approximations for the composite \(\ell_0\) regularization problem (Q1785036) (← links)
• Large-scale hyperspectral image compression via sparse representations based on online learning (Q1787085) (← links)
• Model recovery for Hammerstein systems using the hierarchical orthogonal matching pursuit method (Q1789694) (← links)
• Sparse representation based binary hypothesis model for hyperspectral image classification (Q1792994) (← links)
• \(\ell_1\)- and \(\ell_2\)-norm joint regularization based sparse signal reconstruction scheme (Q1793017) (← links)