The following pages link to Convex low rank approximation (Q1991504):
Displaying 22 items.
- Optimization on low rank nonconvex structures (Q1353375) (← links)
- Convex envelopes for fixed rank approximation (Q1686565) (← links)
- Convex low rank approximation (Q1991504) (← links)
- Lipschitz continuity for isotropic matrix functions (Q2029833) (← links)
- Von Neumann's trace inequality for Hilbert-Schmidt operators (Q2035564) (← links)
- A continuous relaxation of the constrained \(\ell_2-\ell_0\) problem (Q2036185) (← links)
- Efficient proximal mapping computation for low-rank inducing norms (Q2073049) (← links)
- Remove the salt and pepper noise based on the high order total variation and the nuclear norm regularization (Q2079108) (← links)
- Bias versus non-convexity in compressed sensing (Q2155168) (← links)
- On convex envelopes and regularization of non-convex functionals without moving global minima (Q2275270) (← links)
- Tangent and normal cones for low-rank matrices (Q2319935) (← links)
- Low-complexity \(l_0\)-norm penalized shrinkage linear and widely linear affine projection algorithms (Q2405550) (← links)
- Best Nonspherical Symmetric Low Rank Approximation (Q3584143) (← links)
- Real-Valued, Low Rank, Circulant Approximation (Q4443764) (← links)
- Low-Rank Inducing Norms with Optimality Interpretations (Q4554068) (← links)
- A Convex Relaxation to Compute the Nearest Structured Rank Deficient Matrix (Q4994436) (← links)
- An Unbiased Approach to Low Rank Recovery (Q5055687) (← links)
- An unbiased approach to compressed sensing (Q5132273) (← links)
- (Q5139649) (← links)
- Low-Rank Matrix Approximation Using Point-Wise Operators (Q5272166) (← links)
- Low-complexity constrained affine-projection algorithms (Q5355898) (← links)
- A new perspective on low-rank optimization (Q6052053) (← links)
