Pages that link to "Item:Q396052"
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The following pages link to Exact minimum rank approximation via Schatten \(p\)-norm minimization (Q396052):
Displaying 16 items.
- Proximal iteratively reweighted algorithm for low-rank matrix recovery (Q1691320) (← links)
- Multiple kernel low-rank representation-based robust multi-view subspace clustering (Q2126253) (← links)
- Robust image compressive sensing based on half-quadratic function and weighted Schatten-\(p\) norm (Q2201672) (← links)
- Optimal RIP bounds for sparse signals recovery via \(\ell_p\) minimization (Q2330928) (← links)
- Convergence analysis of projected gradient descent for Schatten-\(p\) nonconvex matrix recovery (Q2515314) (← links)
- Stable recovery of low-rank matrix via nonconvex Schatten \(p\)-minimization (Q2629808) (← links)
- On the Schatten \(p\)-quasi-norm minimization for low-rank matrix recovery (Q2659733) (← links)
- Robust subspace clustering based on non-convex low-rank approximation and adaptive kernel (Q2660741) (← links)
- Multi-band weighted \(l_p\) norm minimization for image denoising (Q2666816) (← links)
- Recovery of low rank symmetric matrices via Schatten \(p\) norm minimization (Q2800802) (← links)
- Exact low-rank matrix recovery via nonconvex Schatten \(p\)-minimization (Q2846490) (← links)
- Orthogonalization Via Deflation: A Minimum Norm Approach for Low-Rank Approximations of a Matrix (Q3609023) (← links)
- Approximating minimum norm solutions of rank-deficient least squares problems (Q4940815) (← links)
- Low-Rank Matrix Recovery via Modified Schatten-$p$ Norm Minimization With Convergence Guarantees (Q5105178) (← links)
- Recovery of low-rank matrices based on the rank null space properties (Q5348779) (← links)
- Dual-dual subspace learning with low-rank consideration for feature selection (Q6608256) (← links)
